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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences Jurnal Ilmu Komputer Jurnal Sains dan Teknologi EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnas Nasional Teknologi dan Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Jurnal Pilar Nusa Mandiri JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Tekno Insentif Indonesian Journal of Electrical Engineering and Computer Science JIKA (Jurnal Informatika) Jurnal Responsif : Riset Sains dan Informatika Djtechno: Jurnal Teknologi Informasi Media Jurnal Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Artificial Intelligence and Digital Business Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Sistem Informasi dan Ilmu Komputer Jurnal Informatika Polinema (JIP) Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Toni Arifin
Adhirajasa Reswara Sanjaya (ARS) University
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation

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Emotion Detection in Indonesian Text Using the Logistic Regression Method Erfian Junianto; Mila Puspitasari; Salman Ilyas Zakaria; Toni Arifin; Ignatius Wiseto Prasetyo Agung
Media Jurnal Informatika Vol 17 No 2 (2025): Media Jurnal Informatika
Publisher : 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.
OPTIMASI SUPPORT VECTOR MACHINE MENGGUNAKAN SELEKSI FITUR RANDOM FOREST DAN HYPERPARAMETER GRIDSEARCHCV UNTUK KLASIFIKASI RAISIN DATASET Gregian Bayu Anugrah; Toni Arifin
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.7008

Abstract

Raisin (kismis) merupakan buah kering yang memiliki nilai kandungan gizi yang tinggi dan kaya akan serat. Seiring dengan meningkatnya produksi global, kebutuhan akan sistem klasifikasi otomatis yang akurat menjadi semakin krusial untuk efisiensi industri. Proses klasifikasi manual dinilai tidak efisien, memakan waktu, serta rawan kesalahan manusia, sehingga diperlukan pendekatan berbasis kecerdasan buatan. Penelitian ini bertujuan untuk mengoptimalkan model klasifikasi Support Vector Machine (SVM) dengan penerapan seleksi fitur menggunakan algoritma Random Forest dan optimasi hyperparameter menggunakan GridSearchCV. Dataset yang digunakan adalah Raisin Dataset dari UCI Machine Learning Repository yang terdiri dari 900 sampel dua jenis raisin, yaitu Kecimen dan Besni, masing-masing memiliki tujuh fitur numerik morfologis. Hasil evaluasi menunjukkan bahwa model SVM yang telah dioptimasi mampu mencapai akurasi sebesar 90.00% dan nilai AUC 0,9388. Temuan ini menunjukkan bahwa kombinasi seleksi fitur dan optimasi hyperparameter dapat meningkatkan kinerja model klasifikasi. Penelitian ini juga memiliki perbedaan dari studi sebelumnya dalam hal objek, waktu pelaksanaan, literatur, pendekatan teori, serta hasil akhir yang diperoleh. Pendekatan ini memperkuat pemanfaatan machine learning dalam sektor agroindustri dan membuka peluang pengembangan sistem klasifikasi pangan yang lebih efisien dan presisi.
Comparing Transformer-Based Sentiment Models for Technical-Filtered Trading Signal Generation on NASDAQ-100 Stocks Rifki Ainul Yaqin; Ignatius Wiseto Prasetyo Agung; Toni Arifin; Erfian Junianto
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 3 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

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

Abstract

Predicting stock price movements remains challenging due to nonlinear, dynamic market behavior. News sentiment and technical indicators (RSI, moving averages) each show predictive value, but few studies integrate both within a single, robustly validated framework. This study proposes an algorithmic trading framework integrating daily news sentiment from four pre-trained transformer models (ProsusAI/finbert, nlptown/bert-base-multilingual-uncased-sentiment, yiyanghkust/finbert-tone, and soleimanian/financial-roberta-large-sentiment) with RSI and moving average filters to generate buy signals on 30 NASDAQ-100 stocks. Beyond standard backtesting, the framework is validated through five analyses: benchmark comparison, exit-rule sensitivity, look-ahead-bias alignment, statistical testing, and walk-forward validation. Over a 2-year simulation (57,434 news articles), buy signals were triggered when RSI fell below 40, price pulled back 0.5–5% below MA20, and price remained within 10% above MA50, with entry on day i+1 and a profit-triggered exit. All four models produced positive cumulative returns (123.33–187.84%, Sharpe Ratio 2.00–2.73), remaining profitable under transaction costs up to 0.3% per trade. Benchmarking showed roughly half the Maximum Drawdown of naive baselines despite lower raw returns, favoring selectivity over return maximization. The stop-loss/take-profit variant substantially reduced performance, while stricter news-timestamp alignment left results robust (Sharpe Ratio above 1.5). Statistical tests found three of six model-pair differences significant at the 5% level, though the two top-performing models are statistically indistinguishable (p = 0.546). Out-of-sample evaluation confirmed profitability in the most recent test segment, with acknowledged sub-period variability. These findings position news sentiment as an effective, risk-disciplined complement to technical analysis, with model selection depending on investor priorities.
Co-Authors Ade Mubarok Adi Suparwo Agustin, Dari Dianata Al Huda Ramadhan Ali Akbar Rismayadi Ani Solihat Asri Dwi Andini Asti Herliana, Asti Auliaddina, Siti Chusi Yanasari Daniel Ariesta Dwi Ariyandi Dwiza Riana Dwiza Riana Elin Sopiah Erfian Junianto Erfian Junianto Erfian Junianto Fahmi Permadi Achmad Fajar Nugraha Fakhri Habib Hawari Farizul Ma’arif Faslah Fadillah Fitriyani Fitriyani Fitriyani Fitriyani Gita Indah Hapsari Gregian Bayu Anugrah Hendi Suhendi Hilda Farida Husniah Ignatius Wiseto Prasetyo Agung Ignatius Wiseto Prasetyo Agung Ignatius Wiseto Prasetyo Agung Ignatius Wiseto Prasetyo Agung Ignatius Wiseto Prasetyo Agung Ignatius Wiseto Prasetyo Agung Ihsan Mudzakir Ilham Rachmat Wibowo Imam Muhammad Mannar Shahih Insan Cahya Setia Maya Nurjanah Mayangsari, Ariefa Diah Mayya Nurbayanti Shobary Mila Puspitasari Mila Puspitasari Muhamad Revin Reginal Harahap Muhamad Rifqi Alviandi Muhammad Iqbal Anshori Naufal Hidayah Surya Nur Huzumah Prasetyo Agung, Ignatius Wiseto Puspitasari, Mila Rabbani, Muhammad Ihsan Rahayu Febryani Rahmaniza Maulana Rahmawati Rangga Sanjaya Reddis Angel Rifki Ainul Yaqin Rifki Ainul Yaqin Rina Resmiati Risman Risman Rivanto, Farhan Rizal Rachman, Rizal Rizki Tri Prasetio, Rizki Tri Salman Ilyas Zakaria Salman Ilyas Zakaria Sazila Azka Adzkia Siti Syalwah Sri Mulyani Suhadi, Enda Syarif Hidayatulloh Taufik Hidayatulloh Tiara Nurwulandari Tiki Ramdhani Tyas Widyani Pratiwi Wibowo, Ilham Rachmat Wildan Wiguna Wira Sanjaya Yudi Ramdhani Zakaria, Salman Ilyas