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All Journal Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Informatika Global Jurnal Abdimas Mandiri Reswara: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Jurnal Generic RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Software Engineering and Computational Intelligence
Sartika, Dewi
Universitas Indo Global Mandiri
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Journal : Jurnal Informatika Global

 1 
Prediksi Data Time Series Harga Penutupan Saham Menggunakan Model Box Jenkins ARIMA Imelda Saluza; Dewi Sartika; Lastri Widya Astuti; Faradillah Faradillah; Leriza Desitama; Endah Dewi Purnamasari
Jurnal Informatika Global Vol 12, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v12i2.1940

Abstract

The ability to predict time series data on closing market prices is critical in determining a company's stock results. The development of an efficient stock market has a positive correlation with economic growth, in a country both in the short and long term. In practice, investors tend to invest in countries that have a stable economy, low crime. The rise and fall of stock prices has made many investors develop various effective strategies in predicting stock prices in the future with the aim of making investment decisions so that investors can guarantee their profits and minimize risk.As a result, the researchers developed a model that could accurately estimate precision. Time series data models are one of the most powerful methods to render assumptions in decisions containing uncertainty. The AutoRegressive Integrated Moving Average (ARIMA) model with the Box Jenskins time series procedure is one of the most commonly used prediction models for time series results. The steps for using the Box Jenskins ARIMA model for historical details of expected stock closing prices are outlined in this paper. BBYB and YELO stock data from yahoo.finance were used as historical data. The Aikake Information Criterion (AIC), Bayesian Information Criterion (BIC) / Schawrz Bayesia Criterion (SBC), Log Probability, and Root Mean Square Error (RMSE) are used to choose an effective model, and the model chosen is ARIMA (1 , 1,2). The findings suggest that the Jenkins ARIMA box model has a lot of scope for short-term forecasting, which may help investors make better decisions. Keywords: prediction, the stock's current closing price, Box Jenskins ARIMA model
Analisis Evaluasi Keberlanjutan E-Filling di Kota Palembang Dewi Sartika; Imelda Saluza
Jurnal Informatika Global Vol 9, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (419.113 KB) | DOI: 10.36982/jiig.v9i2.564

Abstract

AbstractDJP continues to optimize the collection of tax returns by facilitating a technology-based tax service system, one of which is e-filing that has been running since 2016. However, e-filing turned out to have less influence on the delivery of Tax Returns (SPT) as reflected in the electronic SPT monitoring data that only met 78% of the 2017 target. This is caused by various problems that arise during the use of e-filing such as individual technology capabilities, loss of efin, forgetting DJP Online account passwords to lack of awareness about the importance of submitting SPT. Problems encounter during the use of e-filing are the basis for evaluating the continued use of e-filling in Palembang. The development of a conceptual model was conducted to evaluate the sustainability of the use of e-filing. The development of a conceptual model basically has a scarcity of supporting theories used and has a complex model. To overcome this problem, Partial Least Squares (PLS) Structural Equation Model (SEM) could be applied to. The results of data analysis found that information quality and service quality did not have a positive influence on the sustainability of the use of e-filing and the level of correlation between information quality, system quality, service quality, and individual ability was still small towards the sustainability of the use of e-filing. The findings of this research are very important for the KPP Pratama in Palembang to analyze the sustainability of the use of e-filing that has been proven empirically, multidimensional and in a specific context. This knowledge could be used as a reference to improve overall quality of taxation for the sake of sustainable use of e-filing.Keywords : SPT, e-filing, PLS SEMAbstrakDJP terus berupaya mengoptimalkan pengumpulan Surat Pemberitahuan Tahunan (SPT) pajak dengan memfasilitasi sistem pelayanan perpajakan berbasis teknologi, salah satunya adalah e-filing yang telah berjalan sejak tahun 2016. Namun, e-filing ternyata kurang berpengaruh terhadap penyampaian SPT yang tergambar pada data hasil monitoring SPT elektronik yang hanya memenuhi 78% dari target sasaran tahun 2017. Hal ini disebabkan oleh berbagai masalah yang muncul selama pemanfaatan e-filing seperti kemampuan teknologi individu, kehilangan efin, lupa password akun DJP Online hingga kurangnya kesadaran tentang pentingnya penyampaian SPT. Permasalahan selama penggunaan e-filing menjadi dasar untuk melakukan evaluasi terhadap keberlangsungan penggunaan e-filling di Palembang. Pengembangan model konseptual dilakukan untuk mengevaluasi keberlanjutan penggunaan e-filing. Pengembangan model konseptual pada dasarnya memiliki kelangkaan teori-teori pendukung yang digunakan dan memiliki model yang kompleks. Untuk mengatasi masalah ini dapat menggunakan Partial Least Squares (PLS) Structural Equation Model (SEM). Hasil analisis data mendapatkan temuan bahwa kualitas informasi dan kualitas layanan tidak memiliki pengaruh positif terhadap keberlanjutan penggunaan e-filing dan tingkat korelasi antara kualitas informasi, kualitas system, kualitas layanan, dan kemampuan individual masih kecil terhadap keberlanjutan penggunaan e-filing. Temuan peneliti ini sangat penting bagi pihak KPP Pratama kota Palembang untuk menganalisa keberlanjutan penggunaan e-filing yang telah dibuktikan secara empiris, multidimensional dan konteks yang spesifik. Pengetahuan ini dapat dapat menjadi acuan untuk meningkatkan kualitas secara keseluruhan demi keberlanjutan penggunaan e-filing.Kata kunci : SPT, e-filing, PLS SEM,
Perbandingan Akurasi Metode Principal Component Analysis (PCA) dan Correlation-Based Feature Selection (CFS) Pada Klasifikasi Perpanjangan Kontrak Karyawan Menggunakan Metode Naïve Bayes Dewi Sartika; Imelda Saluza; Muhammad Haviz Irfani
Jurnal Informatika Global Vol 13, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i2.2292

Abstract

PT. Oasis Waters International Palembang conducts regular staff performance reviews, the findings of which are utilized to make recommendations for employee contract extension. The Human Resource Department has assigned a numerical value to 25 qualities (HRD). The process of giving a label or class to a number of examples when the value of each characteristic is known as classification. The Naïve Bayes technique is a basic classification approach that makes use of probability estimates. Based on the observations, it was discovered that one of the 25 criteria was deemed the most relevant in determining the recommendation for an employee contract renewal. As a result, in this study, a comparison of the pre-processing Principal Component Analysis (PCA) approach and the Correlation-based Feature Selection (CFS) method on the categorization of employee contract extensions at PT Oasis Waters International Palembang will be performed. According to the data, the CFS approach has a positive influence on classification performance, while PCA does not. This is demonstrated by a 30% increase in accuracy when utilizing the CFS approach. Meanwhile, both strategies have a positive influence on the model's dependability. This is demonstrated by a reduction in Root Mean Square Error (RMSE) when using the CFS approach from 0.6325 to 0.1845, whereas using the PCA method results in 0.5123.Keywords : Naïve Bayes, Principal Component Analysis, Correlation-based Feature Selection, Confusion Matrix, Root Mean Square Error
Model Hybrid Menggunakan Dekomposisi-Neural Network Untuk Data Indeks Harga Saham Gabungan Imelda Saluza; Dewi Sartika; Ensiwi Munarsih
Jurnal Ilmiah Informatika Global Vol. 13 No. 3
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i3.2696

Abstract

 The development of Covid-19 has worsened the economy not only nationally but also globally. Since its spread, the price movement of the Jakarta Composite Index (IHSG) has continued to be volatile. JCI price volatility shows risk and uncertainty in investing. Volatility is used as a barometer to determine portfolio management strategies for financial actors. Therefore, financial actors should find a strategy to be able to predict JCI price movements to reduce risks and gain profits. One way that can be done is to predict the JCI price as a reference in investing. This study uses a hybrid model between the decomposition model and the Neural Network (NN) in predicting JCI price volatility. The decomposition uses two approaches, namely additive and multiplicative, the two approaches will then be combined with NN and the NN algorithm used is Feed Forward Neural Network (FFNN) where the results of the decomposition in the form of seasonal, trend, and random data are used as input in the FFNN architecture. The FFNN architecture in this study differs from the hidden layer nodes and the epochs used. Furthermore, the prediction results from the model are compared with a single NN. The performance of each architecture will be measured using the Mean Absolute Error (MAE) and Mean Square Error (MSE). The results show that the hidden layer with more nodes can provide good performance while the epoch used provides good performance depending on the learning process carried out. The prediction results using the hybrid model can outperform the performance of a single NN.Keywords : time series, volatilitas, studi perbandingan, kecerdasan buatan, statistik.
Co-Authors Agustina Heryati Ahmad Sanmorino Anggraini, Leriza Desitama Dhamayanti Dhamayanti Dona Marcelina Endah Dewi Purnamasari Ensiwi Munarsih, Ensiwi Faradillah Faradillah Imelda Saluza, Imelda Indah Permata Sari Indah Permatasari Indah Pratiwi Putri Juhaini Alie Kesuma, Hendra Di Lastri Widya Astuti M. Fadhil Ramadhan Nurbani Muhammad Haviz Irfani Muhammad Haviz Irfani Nazori Suhandi Ratna Meilinda Rendra Gustriansyah Roswaty Roswaty Rudi Heriansyah Shinta Puspasari Sunardi, Hastha Yulianti, Evi Yulius, Yosef Zakaria Saputra