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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences Journal of Information System Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Nasional Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Responsif : Riset Sains dan Informatika Media Jurnal Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Erfian Junianto
Universitas Adhirajasa Reswara Sanjaya
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Transportation

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Stock’s selection and trend prediction using technical analysis and artificial neural network Agung, Ignatius Wiseto Prasetyo; Arifin, Toni; Junianto, Erfian; Rabbani, Muhammad Ihsan; Mayangsari, Ariefa Diah
International Journal of Advances in Applied Sciences Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i1.pp151-163

Abstract

Stock trading offers potential profits when traders buy low and sell high. To maximize profits, accurate analysis is essential for selecting the right stocks, timing purchases, and selling at peak prices. The authors propose a new method for selecting potential stocks that are highly likely to rise in price. The method has two stages. First, technical analysis, using moving averages and stochastic oscillators, filters stocks with downward trends, anticipating a reversal and subsequent rise. Second, for selected stocks, future price trends are predicted using artificial neural networks, specifically long short-term memory (LSTM) with adaptive moment estimation (Adam) optimizer. The second step ensures that only stocks with increasing prices will be chosen for trading. This study analyzes five hundred Fortune 500 stocks over three different periods, with 250 days of data each. Simulations conducted in Python showed that technical analysis could filter 5 to 6 candidate stocks. Subsequently, the LSTM model predicted that only 4 of these stocks would have an upward trend. Validation shows that trend predictions are correct, resulting in an average profit of 5.51% within 10 working days. This profit outperforms the profits generated by other existing methods.
Klasifikasi Emosi pada Teks Berbahasa Inggris Menggunakan Pendekatan Ensemble Bagging Erfian Junianto; Mila Puspitasari; Salman Ilyas Zakaria; Toni Arifin; Ignatius Wiseto Prasetyo Agung
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 4: November 2024
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i4.14440

Abstract

This study highlights the importance of emotion classification in English text, particularly in human interaction on social media, which often involves unstructured data. Emotions play a crucial role in communication; a better understanding of these emotions can aid in analyzing user behavior. The main objective of this research is to enhance accuracy, recall, precision, and F1-score in emotion classification by applying an ensemble bagging approach, combining the naïve Bayes, logistic regression, and k-nearest neighbor (KNN) algorithms. The methodology used included data collection from various sources, followed by data cleaning and analysis using text mining and machine learning techniques. The collected data were then analyzed to detect emotions such as anger, happiness, sadness, surprise, shame, disgust, and fear. Performance evaluation was conducted by comparing the results of the ensemble bagging method with individual algorithms to measure its effectiveness. The findings reveal that the logistic regression method achieved the highest accuracy at 98.76%, followed by naïve Bayes and KNN. This ensemble method overcame the limitations of each individual algorithm, enhancing overall classification stability and reliability. These findings provide valuable insights into text-based emotion analysis techniques and demonstrate the potential of ensemble methods to improve classification accuracy. Future research directions can explore additional ensemble techniques and optimize model complexity for improved performance in emotion analysis across broader datasets.
Breast cancer identification using a hybrid machine learning system Arifin, Toni; Agung, Ignatius Wiseto Prasetyo; Junianto, Erfian; Agustin, Dari Dianata; Wibowo, Ilham Rachmat; Rachman, Rizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3928-3937

Abstract

Breast cancer remains one of the most prevalent malignancies among women and is frequently diagnosed at an advanced stage. Early detection is critical to improving patient prognosis and survival rates. Messenger ribonucleic acid (mRNA) gene expression data, which captures the molecular alterations in cancer cells, offers a promising avenue for enhancing diagnostic accuracy. The objective of this study is to develop a machine learning-based model for breast cancer detection using mRNA gene expression profiles. To achieve this, we implemented a hybrid machine learning system (HMLS) that integrates classification algorithms with feature selection and extraction techniques. This approach enables the effective handling of heterogeneous and high-dimensional genomic data, such as mRNA expression datasets, while simultaneously reducing dimensionality without sacrificing critical information. The classification algorithms applied in this study include support vector machine (SVM), random forest (RF), naïve Bayes (NB), k-nearest neighbors (KNN), extra trees classifier (ETC), and logistic regression (LR). Feature selection was conducted using analysis of variance (ANOVA), mutual information (MI), ETC, LR, whereas principal component analysis (PCA) was employed for feature extraction. The performance of the proposed model was evaluated using standard metrics, including recall, F1-score, and accuracy. Experimental results demonstrate that the combination of the SVM classifier with MI feature selection outperformed other configurations and conventional machine learning approaches, achieving a classification accuracy of 99.4%.
Penerapan Data Mining Metode Apriori Dan FP-Tree pada Penjualan Media Edukasi (Studi Kasus : Oisha Smartkids) Junianto, Erfian; Rachman, Rizal
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 5, No 2 (2020): November 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (787.086 KB) | DOI: 10.31294/ijcit.v5i2.8308

Abstract

Selama ini Oisha Smartkids telah melayani sekian banyak transaksi pesanan produk–produk media edukasi. Setiap data transaksi tersebut disimpan di dalam suatu sistem basis data melalui aplikasi sistem informasi manajemen. Seiring meningkatnya dunia toko online maka informasi mengenai produk-produknya menjadi kebutuhan. Salah satu yang menjadi kebutuhan penting yaitu informasi mengenai penjualan dan persediaan produk media edukasi. Algoritma Apriori termasuk jenis aturan asosiasi pada data mining. Aturan yang menyatakan asosiasi antara beberapa atribut sering disebut affinity analysis atau market basket analysis. Analisis asosiasi atau  association rule mining adalah teknik data mining untuk menemukan aturan suatu kombinasi item. FP-Tree merupakan struktur penyimpanan data yang dimampatkan. FP-tree dibangun dengan memetakan setiap data transaksi ke dalam setiap lintasan tertentu dalam FP-tree. hasil analisa dan pengujian pada transaksi penjualan media edukasi menggunakan data mining dengan algoritma apriori dari 30 data produk, 12 transaksi setiap bulannya selama tahun 2019 menghasilkan nilai minimum support = 25%, nilai minimum confidence 90% dan pola kombinasi produk dan rules sebesar 100%.  Selanjutnya dilengkapi dengan algortma FP-tree menghasilkan 10 produk best seller melalui tahap filterisasi dan menemukan pola kombinasi produk. Sehingga dari 2 metode tersebut sangat penting dalam pengambilan keputusan yang berguna untuk mempersiapkan jenis stok barang apa yang diperlukan kedepanya.So far, Oisha Smartkids has served many transactions for orders for educational media products. Each transaction data is stored in a database system through a management information system application. As the world of online stores increases, information about its products becomes a necessity. One of the important needs is information about sales and inventory of educational media products. Apriori algorithm including the type of association rules in data mining. Rules that state the association between several attributes are often called affinity analysis or market basket analysis. Association analysis or association rule mining is a data mining technique for finding the rules of a combination of items. And FP-Tree is a compressed data storage structure. FP-tree is built by mapping each transaction data into each particular path in FP-tree. analysis and testing results on educational media sales transactions using data mining with a priori algorithm of 30 product data, 12 transactions per month during 2019 resulting in a minimum support value = 25%, a minimum confidence value of 90% and a combination of product and rules pattern of 100%. Furthermore, equipped with FP-tree algortma produces 10 best seller products through the filtering stage and finding patterns of product combinations. So from the 2 methods are very important in making decisions that are useful for preparing what types of goods needed in the future.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SMARTPHONE TERBAIK MENGGUNAKAN METODE SAW Kusnadi, Weli; Kusnadi, Irwan Tanu; Ripandi, Rudi; Junianto, Erfian
Jurnal RESPONSIF: Riset Sains & Informatika Vol 6 No 2 (2024): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v6i2.1631

Abstract

Telepon genggam merupakan alat komunikasi dua arah yang bisa dibawa kemanapun, memiliki kemampuan mengirim pesan dalam bentuk text, suara dan juga dalam bentuk visual atau video. Pada saat ini, banyak sekali manusia yang menggunakan Telepon genggam dari kalangan anak kecil hingga orang dewasa, yang bergantung kepada Telepon genggam untuk kehidupannya, karena pada saat ini segala aktivitas kebanyakan dilakukan secara online. Maka dari itu, perkembangan Telepon genggam setiap tahunnya sangat cepat. Jumlah produk Telepon genggam saat ini sangat banyak dan membuat konsumen bingung memilih Telepon genggam yang tepat untuk keinginan, kebutuhan dan kemampuannya. Tujuan penelitian ini adalah untuk membuat, sistem pendukung keputusan untuk pemilihan Telepon genggam dengan metode simple additive weight (SAW). Selain itu penelitian ini bertujuan untuk mengembangkan sistem penunjang keputusan berbasis web agar dapat mempermudah pengguna dalam memilih telepon genggam yang paling baik, hasil yang didapatkan dalam penelitian ini adalah sistem penunjang keputusan berbasis web yang mampu memberikan kemudahan bagi pengguna dalam mengambil keputusan dalam menentukan telepon genggam.
PENGELOMPOKAN PENGGUNA GAGAL BAYAR PINJAMAN ONLINE PADA MEDIA SOSIAL TWITTER MENGGUNAKAN TF- IDF DAN K-MEANS CLUSTERING Junianto, Erfian; Salshabila, Vini Shiva
JOINS (Journal of Information System) Vol. 10 No. 2 (2025): Edisi November 2025
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v10i2.13652

Abstract

Maraknya layanan pinjaman daring di Indonesia telah mempermudah akses keuangan tetapi juga menyebabkan peningkatan kasus gagal bayar. Kejadian ini tidak dapat semata-mata dikaitkan dengan keterbatasan ekonomi pengguna, sehingga memerlukan eksplorasi yang lebih komprehensif tentang motif yang mendasarinya. Penelitian ini bertujuan untuk mengklasifikasikan motif di balik gagal bayar di kalangan pengguna pinjaman daring berdasarkan wacana publik di Twitter. Sebanyak 2.607 tweet berbahasa Indonesia dikumpulkan menggunakan metode crawling dengan token otorisasi dan alat tweet-harvest. Temuan tersebut mengungkapkan empat tema dominan: tekanan ekonomi dan perilaku keuangan berisiko, promosi yang menyesatkan, intimidasi penagih utang, dan gaya hidup konsumtif. Term Frequency–Inverse Document Frequency (TF-IDF) dan K-Means efektif dalam mengekstraksi informasi yang tidak terstruktur dan mengelompokkan opini publik. Studi ini berkontribusi pada pemahaman perilaku default dan mendukung pengembangan sistem deteksi risiko sosial berbasis teks di masa depan.
Emotion Detection in Indonesian Text Using the Logistic Regression Method Junianto, Erfian; Puspitasari, Mila; Zakaria, Salman Ilyas; Arifin, Toni; Agung, Ignatius Wiseto Prasetyo
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5927

Abstract

Emotion detection in Indonesian text has become a crucial topic in the advancement of human–computer interaction and sentiment analysis on digital platforms. Despite its importance, challenges arise from the linguistic complexity and frequent use of slang in Indonesian text. This study aims to evaluate the performance of three classification models—Logistic Regression, K-Nearest Neighbors (KNN), and Naive Bayes—in detecting emotions from Indonesian text. The dataset comprises 1,000 texts categorized into four emotions: happy, sad, angry, and fear. Preprocessing steps included slang normalization, text cleaning, tokenization, stopword removal, and stemming, followed by TF-IDF weighting. Each model was trained and further optimized using ensemble bagging to improve classification performance. The optimized Logistic Regression model achieved the best performance, with an accuracy of 89%, precision of 0.90, recall of 0.89, F1-score of 0.89, and an average ROC-AUC score of 0.98. Both KNN and Naive Bayes models reached 81% accuracy after optimization, but their overall performance remained lower than Logistic Regression. The findings demonstrate that Logistic Regression is the most effective method for detecting emotions in Indonesian text, as it can effectively handle simple grammatical structures and slang variations. This study contributes to the development of emotion analysis models for Indonesian text, supporting applications in social computing and affective computing.
Stock Price Forecasting Using LSTM with Cross-Validation Rifki Ainul Yaqin; Anshori, Muhammad Iqbal; Angel, Reddis; Agung, Ignatius Wiseto Prasetyo; Arifin, Toni; Junianto, Erfian
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 1 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i1.45130

Abstract

Stock price prediction remains challenging due to the market’s nonlinear, volatile nature, influenced by diverse economic and behavioral factors. Traditional models often suffer from overfitting and limited generalizability. This study addresses these limitations from prior research by other researchers for integrating Long Short-Term Memory (LSTM) with k-Fold Cross-Validation to improve prediction robustness. The proposed framework systematically evaluates model performance across varying market conditions. This methodological contribution enhances forecasting accuracy and stability, offering a more reliable approach to complex financial time series prediction. This study employs LSTM with one to two layers of 64–128 units, trained using Adam and dropout regularization, to capture long-term dependencies in stock price data. The workflow integrates feature selection, Min-Max scaling, and k-Fold Cross-Validation for robust evaluation. Model performance is assessed using RMSE, with reconfiguration applied to address underfitting or overfitting. The proposed model demonstrated substantial performance gains, achieving an average RMSE improvement of approximately 78.40% across all tested stocks compared to prior research. These enhancements are attributed to optimal hyperparameter tuning, consistent use of the Adam optimizer, and the implementation of k-Fold Cross-Validation, which reduced overfitting and provided more stable evaluations. Furthermore, findings revealed that simpler feature sets, such as using only closing prices, can outperform multiple technical indicators when normalization is inadequate, underscoring the importance of robust preprocessing and validation strategies. This study concludes that integrating LSTM with k-Fold Cross-Validation and optimized hyperparameters significantly improves stock price prediction accuracy.
Co-Authors Abdul Rahman Wahid Rapsanjani Ade Mubarok Agung Baitul Hikmah Agustin, Dari Dianata Ali Akbar Rismayadi Ali Akbar Rismayadi Alif Muhamad Nisar Angel, Reddis Anshori, Muhammad Iqbal Arif Rifan Hidayat, Arif Rifan Dwiza Riana Fathin Sophosta Tocas Hari Heryadi Iedam Fardian Anshori, Iedam Fardian Ignatius Wiseto Prasetyo Agung Ignatius Wiseto Prasetyo Agung Irwan Tanu Kusnadi Kusnadi, Weli Mayangsari, Ariefa Diah Mila Puspitasari Muhamad Iqbal Maulana Muhammad Yoga Pratama Muhammad Zaid Zuhdi Nadya Paramitha Novia Nur Fatonah Prasetyo Agung, Ignatius Wiseto Puspitasari, Mila Rabbani, Muhammad Ihsan Rifki Ainul Yaqin Ripandi, Rudi Rizal Rachman, Rizal Rizki Tri Prasetio Salman Ilyas Zakaria Salman Topiq Salshabila, Vini Shiva Sari Susanti Syarif Hidayatulloh Tessa Putri Mallini Toni Arifin Toni Arifin Wibowo, Ilham Rachmat Yogi Aditya Pratama Yudi Ramdhani Yusa Primaesha Zakaria, Salman Ilyas