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All Journal Indonesian Journal on Computing (Indo-JC) JOIN (Jurnal Online Informatika) JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science JINAV: Journal of Information and Visualization eProceedings of Engineering
Indwiarti, Indwiarti
Telkom University, Bandung
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics

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Implemetasi Model Autoregressive (AR) Dan Autoregressive Conditional Heteroskedasticity (ARCH) Untuk Memprediksi Harga Emas Ni Luh Ketut Dwi Murniati; Indwiarti Indwiarti; Aniq Atiqi Rohmawati
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 2 (2018): September, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.2.225

Abstract

Gold is a one of  high selling value items in the market, and it  can be used as an investment item. The price of gold in the market tends to be stable and not undergoing too significant changes which makes gold be a very valuable item. The aim of this research is to predict gold price using AR (1) and ARCH (1) model which are the part of time series methods. The data of gold price is obtained from ANTAM's daily historical website from 2007 - 2017. Here, the basic information about data is given by using descriptive statistic and the estimation of parameters in each model is condacted by using Maximum Likelihood Estimation (MLE). To evaluate the model, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used. In this research, the estimated model of AR (1) and ARCH (1) given as X_t = -0.012X_{t-1}+epsion_t and X_t = epsilon_t sqrt{0.000053+0.011958X^2_{t-1}} respectively. Moreover, the result of MAE and RMSE using AR (1) model are 0.0261 and 0.0342 respectively, meanwhile for ARCH (1) model  are 0.0170 and 0.0251 respectively.
Perbandingan Prediksi Harga Saham dengan model ARIMA dan Artificial Neural Network Bagas Yafitra Pandji; Indwiarti Indwiarti; Aniq Atiqi Rohmawati
Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 2 (2019): September, 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2019.4.2.344

Abstract

Kondisi perekonomian dunia mengalami perubahan yang signifikan, mayoritas merupakan dampak dari kenaikan minyak dunia, karena minyak merupakan sumber energi utama di dunia. Kondisi tersebut juga berimbas pada harga saham di pasar modal. Ada beberapa variabel yang mempengaruhi nilai return saham, yang sifatnya linier dan non-linier terhadap return harga saham. Untuk memodelkan observasi yang linier digunakan model time series Autoregressive Moving Average (ARIMA). Selanjutnya, untuk observasi yang non-linier digunakan Artificial Neural Network (ANN). Pada penelitian ini didapatkan perbandingan hasil perhitungan error RMSE dengan model ARIMA (1, 0, 0), dan ARIMA (2, 0, 0), masing-masing sebesar 1,3738, 1.5514, sedangkan ANN dengan 16 hidden layer sebesar 4.6814. Hasil dari penelitian ini model ARIMA (1, 0, 0) lebih akurat dibandingkan metode ANN dalam prediksi harga saham PT. Bumi Citra Permai Tbk.Kata Kunci: ARIMA, ANN, Prediksi, Saham
Implementasi Genetic Algorithm dalam Model ARIMA untuk Memprediksi Observasi Time Series Rangga Arya Pamungkas; Indwiarti Indwiarti; Aniq Atiqi Rohmawati
Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 3 (2019): December, 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2019.4.3.353

Abstract

Nilai harga saham selalu berubah-ubah dan berfluktuasi setiap harinya. Untuk menghadapi masalah mengenai ketidakpastian harga saham, perlu dilakukan suatu peramalan time series untuk memprediksi harga saham di masa mendatang. Pada penelitian ini, metode yang digunakan untuk memprediksi harga saham adalah metode Autoregressive Moving Average (ARIMA). Untuk meningkatkan akurasi dari prediksi harga saham, akan diimplementasikan Genetic Algorithm (GA) pada model ARIMA terbaik yang didapatkan dari proses ARIMA. Hasil dari penelitian ini menunjukkan bahwa prediksi harga saham dengan menggunakan model ARIMA (1,1,1) memiliki nilai Root Mean Square Error (RMSE) sebesar 418.1314. Sedangkan hasil prediksi harga saham dengan mengimplementasikan GA pada model ARIMA (1,1,1) dengan 600 generasi, 1200 generasi, 1800 generasi, 2400 generasi, dan 3000 generasi masing-masing memiliki nilai RMSE berturut-turut sebesar 5827.738, 1319.903, 1080.704, 563.7984, dan 371.0107. Hasil yang didapat menunjukkan bahwa pengimplementasian GA pada ARIMA dengan 3000 generasi dapat meningkatkan akurasi prediksi harga saham, yaitu dengan memiliki nilai RMSE sebesar 371.0107.Kata Kunci: GA, Harga Saham, Model ARIMA, Prediksi, RMSE
Clustering of Earthquake Prone Areas in Indonesia Using K-Medoids Algorithm Fiona Ramadhani Senduk; Indwiarti Indwiarti; Fhira Nhita
Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 3 (2019): December, 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2019.4.3.359

Abstract

Located right above the ring of fire makes Indonesia prone to natural disasters, especially earthquakes. With the number of earthquakes that have occurred, disaster mitigation is very much needed. The use of data mining methods will certainly help in disaster mitigation. One method that can be used is clustering. The clustering algorithm used in this study is k-Medoids, and comparison with the k-means algorithm is also carried out. The data used are earthquake data from all regions in Indonesia during 2014-2018 that were recorded by the United State Geological Survey (USGS). The results obtained showed that k-medoids giving better silhouette results and computational time than k-means. For the k-medoids cluster results, the highest value of silhouette was 0.4574067 with k = 6. The analysis of each cluster is presented in this paper.Keywords: clustering,data mining, earthquake, k-medoid.
Penerapan Analisis Klaster untuk Seleksi Aset dalam Optimasi Portofolio Investasi Saham varid vaya yusuf; irma palupi; indwiarti indwiarti
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 2 (2020): September, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.2.438

Abstract

Manfaat diversifikasi dapat dioptimalkan dengan mengategorikan aset ke dalam kelas-kelas tertentu. Di dalam pasar keuangan, terdapat struktur hirarki antar saham dan dapat dianalisis dengan mengobservasi serangkaian harga saham yang saling berkorelasi. Penelitian terdahulu banyak berfokus pada dampak analisis klaster terhadap performa portofolio, namun sedikit yang meninjau sisi seleksi aset dalam benchmark-nya. Penelitian ini mengajukan tiga skenario alternatif seleksi aset untuk proses konstruksi portofolio berbasis klaster sebagai sudut pandang baru dalam penyusunan benchmark konstruksi portofolio. Dalam pelaksanaannya, digunakan ward’s method untuk melakukan klasterisasi terhadap saham berdasarkan data in-sample dari 606 perusahaan tercatat di BEI. Dilanjutkan dengan konstruksi portofolio dengan tangency portfolio sebagai preferensi portofolio optimal dan seleksi aset dengan tiga skenario alternatif. Performa portofolio diukur menggunakan rasio Sharpe dan rasio terhadap data out-sample. Analisis klaster yang dilakukan menunjukkan kualitas yang luar biasa dalam kelompokkelompok saham yang terbentuk. Portofolio dengan analisis klaster memberikan performa yang sangat baik, melebihi portofolio tanpa analisis klaster.
Trading Strategy on Market Stock by Analyzing Candlestick Pattern using Artificial Neural Network (ANN) Method Ni Putu Winda Ardiyanti; Irma Palupi; Indwiarti Indwiarti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3266

Abstract

Technical analysis plays an important role in a stock market. Traders using technical analysis to find the trading strategy on the market stock. There are some technical indicators tools that can support the technical analysis, such as Moving Average, Stochastic, and others. Candlestick pattern also parts of the tools that used in technical analysis to develop the trading strategy since Candlestick represents the stock behavior. Therefore, understanding the Candlestick pattern and technical indicator tools will be valuable for the traders to predict the trading strategy. This study performs the prediction of trading strategy by analyzing the Candlestick pattern using an Artificial Neural Network (ANN). The technical indicator tools and Candlestick pattern will be generated as the features and label data in the modeling process. The method is applied to four stocks from IDX through their technical indicators for a certain period of time. We find that in the period of 28 days, the model generates the highest accuracy that reached 85.96%. We also used K-Fold Cross-Validation to evaluate the result of model performance that generates
Sentiment Analysis of Hate Speech on Twitter Public Figures with AdaBoost and XGBoost Methods Daffa Ulayya Suhendra; Jondri Jondri; Indwiarti Indwiarti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4394

Abstract

Public figures are often scrutinized by social media users, either because of what they say or even because of their role in a television series. Generally, public figures upload something on their social media accounts to help shape their image. But not everyone who sees it is happy. Some even dislike the upload. This study aims to determine public sentiment towards public figure Anya Geraldine conveyed on Twitter in Indonesian. The classification process in this study uses the Adaptive Boosting (AdaBoost) and Extreme Gradient Boosting (XGBoost) classification methods with text preprocessing using cleaning, case folding, tokenization, and filtering. The data used are tweets in Indonesian with the keyword ”@anyaselalubenar”, with a total dataset of 7,475 tweets divided into 6,887 positive and 588 negative tweets. From the label results using oversampling to avoid excessive overfitting problems. The feature used is TF-IDF weighting. Four experimental scenarios were carried out to validate the effectiveness of the model used: first model performance without oversampling, second model performance with oversampling, third model performance with undersampling, and fourth model performance with Hyperparameter tune. The experimental results show that XGBoost+SMOTE+Hyperparameter achieved 95% compared to AdaBoost+SMOTE+Hyperparameter of 87%. The application of SMOTE and Hyperparameter tune is proven to overcome the problem of data imbalance and get better classification results.
Prediction Retweet Using User-Based and Content-Based with ANN-GA Classification Method Edvan Tazul Arifin; Jondri Jondri; Indwiarti Indwiarti
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1931

Abstract

Current technological advances have caused rapid dissemination of information, especially on social media, one of which is Twitter. Retweeting or reposting messages is considered an easily available information diffusion mechanism provided by Twitter. By finding out why a user retweets a tweet from another person and by making this prediction we can understand how information diffuses on Twitter. In this study, Artificial Neural Network – Genetic Algorithm is used in the classification process and uses user-based and Content-Based features. Evaluation result obtained in this study are 90% accuracy, 72% precision, 83% recall, and 65% F1-Score value on the model by Oversampling.
Retweet Predictions Regarding COVID-19 Vaccination Tweets through The Method of Multi Level Stacking Vena Erla Candrika; Jondri Jondri; Indwiarti Indwiarti
JINAV: Journal of Information and Visualization Vol. 4 No. 1 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1518

Abstract

The rapid development of technology from day to day indirectly influences increasing social media use. This can be seen from spreading information that is very easily found on social media, one of which is Twitter. It is one of the most popular platforms for expressing people’s feelings by tweeting and interacting with other users at the same time. Various opinions about the COVID-19 vaccination began to be discussed on the Twitter platform. Moreover, most people take advantage of the feature available on Twitter, namely retweets. Users do retweet because there are many influencing factors. It can be caused by a reason that they have the same opinions and thoughts as the tweet owner, and so on. A retweet feature is also a form of information diffusion on the Twitter platform. The diffusion of information on Twitter has several factors, such as the most influential users, using hashtags or URLs, and others. In this conclusion, retweet predictions have been carried out regarding COVID-19 vaccination tweets using the features user-based and time-based through the Multi-Level Stacking classification method. This method indicates the best results when oversampling with an F1-Score of 96.23%.
Analisis Seleksi Mahasiswa Baru Jalur Non-tulis Menggunakan Algoritma Ant-miner M. Abdul Jabbar; Indwiarti Indwiarti; Fitriyani Fitriyani
eProceedings of Engineering Vol 2, No 3 (2015): Desember, 2015
Publisher : eProceedings of Engineering

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

Abstract

Seleksi mahasiswa baru jalur non-tulis adalah salah satu dari jalur masuk Universitas dengan jumlah pendaftar yang tinggi. Tugas akhir ini membahas bagaimana cara melakukan seleksi mahasiswa baru jalur non-tulis dengan menggunakan rules yang didapat dari metode klasifikasi. Diharapkan rules hasil klasifikasi dapat digunakan untuk membantu mengevaluasi penerimaan mahasiswa baru dari jalur non-tulis. Klasifikasi terhadap data seleksi mahasiswa baru jalur non-tulis dapat dilakukan dengan algoritma ant-miner. Ant-miner (ant-colony based data miner) adalah algoritma yang digunakan untuk mengekstrak rules klasifikasi dari data dan telah memberikan hasil yang cukup memuaskan dalam beberapa jenis data kompleks yang telah diujikan. Tugas akhir ini juga bertujuan untuk menghasilkan analisis dari penggunaan algoritma ant-miner terhadap data seleksi mahasiswa baru jalur non-tulis. Hasil penelitian tugas akhir ini menunjukkan bahwa algoritma ant-miner menghasilkan akurasi training dan testing yang cukup baik, tidak overfitting, dan menghasilkan rules dengan akurasi, recall dan presisi yang baik sehingga dapat digunakan untuk mengevaluasi penerimaan mahasiswa baru jalur non-tulis. Kata kunci : Klasfikasi, Seleksi Mahasiswa Baru, Ant-colony optimization, Ant-miner
Co-Authors Aniq Atiqi Rohmawati Annisa Aditsania Bagas Yafitra Pandji Cici Olivia Daffa Ulayya Suhendra Dika Rizky Nurcholis Dyas Puspandari Edvan Tazul Arifin Erni Sumaryati Fhira Nhita Fiona Ramadhani Senduk Fitriyani Fitriyani Ika Nugraheni Irma Palupi irma palupi Jondri Jondri Jondri Jondri Jondri Jondri Justinus Dedy Handyka Simanjuntak M. Abdul Jabbar May Rozakhi Takkas Mochamad Ikbal Ashari Muhammad Rizqi Akbar Ni Luh Ketut Dwi Murniati Ni Putu Winda Ardiyanti Rafik Khairul Amin Rangga Arya Pamungkas Rian Febrian Umbara Rica Ning Nurhasanah Rizky Farida Utami Rizky Rahmattullah Samuel Erlangga Tegar Tionanda Putra varid vaya yusuf Vena Erla Candrika Yulian Sibaroni Yuliant Sibaroni