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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Prosiding Seminar Nasional Ilmu Teknik
Aziz Musthofa
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Analisis Sentimen Publik pada TikTok terhadap Rencana Penerapan Sistem Balik Nama Ponsel Bekas menggunakan Naive Bayes dan Support Vector Machine Afif Lustyo Muji; Aziz Musthofa; Dihin Muriyatmoko
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.198

Abstract

Since the announcement of the policy plan for a name transfer system in the sale of used mobile phones, the issue has attracted widespread public attention and discussion. People have expressed their opinions on social media platforms, particularly TikTok. This study aims to classify the sentiment of TikTok users using Naive Bayes and Support Vector Machine (SVM) algorithms. The data were collected through a comment scraping technique on related content.The research stages include text preprocessing, sentiment labeling into positive, negative, and neutral categories, and feature extraction using TF-IDF. The classification process employs Naive Bayes and Support Vector Machine algorithms, which are then evaluated based on accuracy, precision, recall, and F1-score. The results of this study indicate that both methods are capable of classifying sentiment effectively. However, the Support Vector Machine method is superior to the Naive Bayes method with an accuracy rate of 99.57% compared to 94.30%. This study is expected to help the government understand public responses to the planned policy of the used mobile phone name transfer system.
Prediksi Penjualan Multi-Produk Menggunakan Metode Long Short-Term Memory (LSTM) pada Data Point of Sale (POS) (Studi Kasus Toko Sahabat Baru di Kabupaten Blitar) Aziz Musthofa; Novanto Yudistira
Jurnal Pengembangan Teknologi dan Ilmu Komputer Vol 10 No 7 (2026): Juli 2026
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Prediksi penjualan pada lingkungan retail penting untuk mendukung demand planning dan pengendalian persediaan. Data penjualan berbasis Point of Sale (POS) dapat dimanfaatkan sebagai dasar prediksi, namun umumnya memiliki karakteristik fluktuatif, mengandung banyak periode tanpa transaksi (intermittent demand), serta menunjukkan pola permintaan yang berbeda antar produk. Kondisi tersebut meningkatkan kompleksitas prediksi dan membatasi efektivitas metode statistik tradisional. Penelitian ini bertujuan menganalisis performa model Long Short-Term Memory (LSTM) dalam prediksi penjualan multi-produk berbasis data POS serta membandingkannya dengan ARIMA dan naive forecasting. Dataset penelitian terdiri atas 4.279 transaksi dari 25 produk dan 9 kategori selama periode Maret 2025 hingga Februari 2026. Setelah transformasi full grid dan imputasi nol, jumlah observasi meningkat menjadi 9.125 data dengan tingkat sparsity sebesar 53,11%. Hasil penelitian menunjukkan bahwa konfigurasi terbaik diperoleh pada rasio split 70:20:10 dengan parameter look-back = 7 dan units = 16, menghasilkan MAE sebesar 2,2690, RMSE sebesar 3,1621, dan MASE sebesar 0,8816. Secara keseluruhan, LSTM menghasilkan performa lebih baik dibandingkan ARIMA dan naive forecasting. Hasil prediksi juga dapat digunakan sebagai dasar analisis demand planning.
Co-Authors Afif Lustyo Muji Dihin Muriyatmoko Novanto Yudistira