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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Seminar Nasional Teknologi Informasi Komunikasi dan Industri Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research SATIN - Sains dan Teknologi Informasi
Elin Haerani
Jurusan Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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SISTEM PAKAR UNTUK DIAGNOSIS AWAL INDIKASI GANGGUAN KECEMASAN MENGGUNAKAN METODE CERTAINTY FACTOR Nurul Ikhsan Siahaan; Fitri Wulandari; Elin Haerani; Yelfi Vitriani; Fitra Kurnia; Ikhwanisifa
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 10 No. 02 (2025): Volume 10 No. 02 Juni 2025 In Build
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v10i02.26422

Abstract

Penderita gangguan kecemasan mengalami ketakutan dan kekhawatiran berlebihan yang sulit dikendalikan, disertai ketegangan fisik serta gejala perilaku dan kognitif. Jika tidak ditangani, kondisi ini dapat bertahan lama dan menimbulkan tekanan yang signifikan. Penelitian ini bertujuan untuk merancang dan mengembangkan sebuah sistem pakar yang berfungsi dalam memberikan diagnosis awal gangguan kecemasan dengan memanfaatkan pendekatan metode Certainty Factor. Penelitian ini dilaksanakan melalui tahapan identifikasi permasalahan, pengumpulan informasi, analisis kebutuhan, pembuatan pohon inferensi, perancangan sistem, proses implementasi, serta pengujian kinerja sistem, dengan menggunakan 4 data penyakit dan 58 data gejala. Sistem berhasil melakukan diagnosis gangguan kecemasan dengan tingkat akurasi sebesar 87,5%. Hasil pengujian memperlihatkan bahwa seluruh fitur sistem berjalan sesuai harapan. Sistem pakar ini mampu mendukung proses diagnosis awal gangguan kecemasan sekaligus menyediakan sumber informasi yang bermanfaat bagi pengguna.
Penerapan Metode Support Vector Machine Untuk Analisis Sentimen Pada Komentar Bitcoin Di Aplikasi X Yaskur Bearly Fernandes; Elin Haerani; Fadhilah Syafria; Muhammad Fikry; Lola Oktavia
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.928

Abstract

Social media has become a primary medium for users to express opinions, including those related to Bitcoin, whose fluctuating value often triggers diverse public responses. The large volume of unstructured comments makes manual sentiment analysis inefficient, thereby necessitating an automated approach based on machine learning. This study aims to classify positive and negative sentiments in Bitcoin-related comments on the X platform using the Support Vector Machine (SVM) algorithm with Term Frequency–Inverse Document Frequency (TF-IDF) feature weighting. The dataset consists of 1,750 Indonesian-language comments labeled by three annotators. The data were processed through several preprocessing stages, including case folding, text cleaning, tokenization, stopword removal, and stemming. Model evaluation was conducted using four data split ratios, namely 90:10, 80:20, 70:30, and 60:40. The experimental results indicate that the 90:10 ratio achieved the best performance, with an accuracy of 72.57%, precision of 0.75, recall of 0.73, and an F1-score of 0.67. The SVM model demonstrates strong performance in identifying positive sentiments; however, it is less effective in detecting negative sentiments due to class imbalance in the dataset. As an additional experiment, testing was performed using a balanced dataset obtained through an undersampling process and several SVM kernel types for comparison. The results show that using a balanced dataset leads to more evenly distributed classification performance across sentiment classes, while the linear kernel provides the most stable performance compared to other kernels. Overall, SVM with TF-IDF weighting proves to be an effective approach for sentiment analysis of Bitcoin-related comments on social media.
Co-Authors Alwis Nazir Fadhilah Syafria Febi Nur Salisah febi nursalisah Fitra Kurnia Fitri Insani Fitri Wulandari Fitri Wulandari Iis Afrianty Ikhwanisifa Iwan Iskandar Liza Afriyanti Lola Oktavia Muhammad Affandes Muhammad Fikry Muhammad Hanif Abdurrohman Muhammad Ichsanul Bukhari Muhammad Rizki Syafapri Nailatul Fadhilah Novrianto novrianto Nurul Ikhsan Siahaan Pizaini Pizaini Purnama, Nilam Putri Mardatillah Rice Novita Rice Novita Rometdo Muzawi, Rometdo Teddie Darmizal Yaskur Bearly Fernandes Yelfi Vitriani Zuriati Ardila Safitri