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All Journal Jurnal Sains dan Teknologi Journal of Information Systems Engineering and Business Intelligence Tropical Animal Science Journal Pendas : Jurnah Ilmiah Pendidikan Dasar BISEI: Jurnal Bisnis dan Ekonomi Islam bit-Tech Jurnal Cahaya Mandalika Hukum Responsif : Jurnal Ilmiah Fakultas Hukum Universitas Swadaya Gunung Jati Cirebon Journal of Science and Education (JSE) Abdi-Dharma East Asian Journal of Multidisciplinary Research (EAJMR) Jurnal Agribis Journal Of Science Education And Management Business Bandung Conference Series: Accountancy JURNAL PILAR KEADILAN Journal of Research and Publication Innovation MAJAPAHIT POLICY Jurnal Industrikrisna Jurnal Krakatau (Indonesian of Multidiciplinary Journal) AGRIC Jurnal Ekonomi Bisnis Manajemen dan Akuntansi (Jebisma) Jurnal Pengabdian Masyarakat dan Riset Pendidikan El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam LEX LAGUENS: Jurnal Kajian Hukum dan Keadilan Journal of Ekonomics, Finance, and Management Studies Indonesian Journal of Humanities and Social Sciences STIKes BBM Mengabdi: Jurnal Pengabdian Masyarakat Journal Social Civilecial Jurnal Pendidikan Islam Interaction Lentera: Journal of Islamic Studies
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Journal : bit-Tech

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Eksplorasi Algoritma Support Vector Machine untuk Analisis Sentimen Destinasi Wisata di Indonesia Junaedi; Hendra Gunawan, Alexius; Kuswanto, Verri; Jonathan
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1810

Abstract

Penelitian ini mengeksplorasi penerapan algoritma Support Vector Machine (SVM) dalam Text Mining untuk analisis sentimen sektor pariwisata di Indonesia, menggunakan data dari platform Twitter. Data dikumpulkan melalui API Twitter dan diproses melalui tahapan prapemrosesan teks, termasuk tokenisasi, normalisasi, penghapusan stopword, dan stemming, untuk memastikan kesiapan data dalam analisis. Model SVM diuji dengan tiga kernel berbeda—linear, radial basis function (RBF), dan sigmoid—serta menggunakan rasio data latih-uji 7:3 dan 8:2. Hasil menunjukkan bahwa kernel linear dengan rasio 7:3 menghasilkan kinerja terbaik dengan akurasi 92,89%, precision 92%, recall 74%, dan F1-score 81%. Evaluasi berdasarkan kelas sentimen menunjukkan performa tinggi pada sentimen positif (F1-score 96%) tetapi moderat pada kelas netral (F1-score 67%), mencerminkan pengaruh ketidakseimbangan data. Penelitian ini memberikan kontribusi signifikan dalam mendukung pengambilan keputusan berbasis data untuk pengembangan sektor pariwisata. Temuan ini memungkinkan pengelola destinasi wisata untuk memahami opini wisatawan secara otomatis, menyusun strategi promosi yang lebih efektif, serta meningkatkan kualitas layanan. Dengan menerapkan analisis sentimen berbasis SVM, penelitian ini mendukung pengelolaan pariwisata berbasis data untuk meningkatkan daya saing destinasi wisata di Indonesia. Penelitian lanjutan disarankan untuk mengatasi ketidakseimbangan data melalui teknik resampling atau penerapan algoritma alternatif seperti deep learning, guna meningkatkan akurasi klasifikasi sentimen yang lebih kompleks. Dengan demikian, penelitian ini menjadi langkah strategis dalam memanfaatkan teknologi analitik untuk pengelolaan pariwisata yang lebih inovatif.
Enhancing Stock Price Forecasting: Optimizing Neural Networks with Moving Average Data Hermawan, Aditiya; Ananda, Stanley; Junaedi; Edy
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2196

Abstract

This research focuses on optimizing a neural network model for stock price prediction using Particle Swarm Optimization (PSO), considering the inherent risks and potential high returns associated with stock investment. Given the challenges posed by stock price volatility, this study combines Moving Average (MA) a fundamental statistical technique in stock market analysis with advanced data mining approaches, specifically neural networks and PSO, to enhance prediction accuracy. The primary objective is to improve the efficiency of neural networks by minimizing error rates and equipping investors with more reliable tools for financial decision-making. The proposed methodology involves converting historical stock price data into a Simple Moving Average (SMA) over a 5-day period, followed by optimizing a neural network model using PSO. This optimization process fine-tunes key parameters, particularly the weight distributions of various stock market indicators, including Open SMA, High SMA, Low SMA, and Close SMA. Model performance is evaluated using Root Mean Square Error (RMSE) as a validation metric. The findings indicate a significant enhancement in the predictive accuracy of the neural network model after PSO optimization. The optimal configuration is identified in a two-layer neural network with a specific node arrangement. This optimized model not only improves stock price forecasting precision but also has practical implications for investors and financial analysts in risk management and profit maximization.
Co-Authors Abdul Wahab Aditiya Hermawan Ahmad Sukandar Ahmad Taufik Ahmad Yusuf Andrian Putra Akhmad Syahid Ambarsari, Dewi Ananda, Stanley Anang Rohmad Jatmiko Andi Bulqiah Nur Bunyamin Andi Bunyamin Andri Wijaya Arman Asnun Parwanti Bachtiar, Machdum Bachtillah, Andriansyah Benny Daniawan Ceng Giap, Yo Daniawan, Benny Dede Andi Dewi Oktavia Diki Dikrurahman edy Fattah, A. H. Fauzi, Agam G. Ciptadi Habeahan, Rasman Halim Wijaya, Ardie Hanun Asrohah Hendra Gunawan, Alexius Hermawan , Aditiya Irninthya Nanda Pratami Irwan Iskandar Ritonga Iswara, Aditya Johan Santoso Jonathan Junaenah Khaeruddin Khaeruddin Kristen Kumala, Sonya Ayu Kurnia , Yusuf Kurniawan Maranto, Ardiane Rossi Kuswanto, Verri Lunardi, Lidya Lutfiah ur Huda Watri M. Yusuf Machrus Ali Mawar Kartina, Ratu Mey Maemunah Mone, Ansyari Muh Nasir Muhamad Ali Muhammad Afandi Arifin N. Isnaini Naharuddin Naharuddin Nailatuzzuhra Natsir, M. H. Nur Aeni F Nurhapsa Nuridha Rachmat, Ade Dian Pupung Purnamasari Purnomo, Azis Riki Rino Risnandar, Aep Rosita, Yeti Rossi , Ardiane Rossi Kurniawan Maranto, Ardiane Rossi Ryo Setyawan S. Wahjuningsih Sugeng Prayitno Supriyatno Suwitno Syahid, Adi Syahruni Thamrin, Syahruni Syamsuryadi, B. T. Susilawati Tia Nurapriyanti Wydiastuty Kusuma, Lianny Yusuf Kurnia Yusuf Kurnia, Yusuf Zohriah, Anis