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All Journal Jurnal Pseudocode Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Infomedia JURNAL PengaMAS MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Information System Journal (INFOS) JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Pengabdian Mitra Masyarakat (JPMM) International Journal of Advanced Science Computing and Engineering SmartComp Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Scientific Journal of Informatics SWAGATI: Journal of Community Service Jurnal Pengabdian Masyarakat Inovasi Indonesia
Yoga Pristyanto
Universitas Amikom Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Comparative Performance of SVM and Multinomial Naïve Bayes in Sentiment Analysis of the Film 'Dirty Vote' Iedwan, Aisha Shakila; Mauliza, Nia; Pristyanto, Yoga; Hartanto, Anggit Dwi; Rohman, Arif Nur
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.10290

Abstract

Purpose: The purpose of this research is to analyze and compare the performance of two machine learning models, Support Vector Machine (SVM) and Multinomial Naive Bayes, in conducting sentiment analysis on YouTube comments related to the film "Dirty Vote." Methods: The study involved collecting YouTube comments and preprocessing the data through cleaning, labeling, and feature extraction using TF-IDF. The dataset was then divided into training and testing sets in an 80:20 ratio. Both the SVM and Multinomial Naive Bayes models were trained and tested, with their performance evaluated using accuracy, precision, recall, and F1-score metrics. Result: The results revealed that both models performed well in classifying sentiments, with SVM slightly outperforming Multinomial Naive Bayes in terms of accuracy and precision. Particularly, SVM showed superior performance in detecting positive comments, making it a more reliable model for this specific sentiment analysis task. Novelty: This study contributes to the field of sentiment analysis by providing a detailed comparative analysis of SVM and Multinomial Naive Bayes models on YouTube comments in the context of an Indonesian film. The findings highlight the strengths and weaknesses of each model, offering insights into their applicability for sentiment analysis tasks, particularly in analyzing social media content. This research also suggests potential future directions, including the exploration of advanced NLP techniques and different models to enhance sentiment analysis performance.
Otomatisasi Penerusan Laporan Pengaduan Menggunakan Neural Network Khoiruddin, Lukman; Sidauruk, Acihmah; Pristyanto, Yoga; Yudiyanto, Muhammad Resa Arif; Kurniawan, Hendra
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 13, No 2 (2024): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v13i2.6662

Abstract

Saat ini terdapat sistem laporan pengaduan masyarakat yang sudah terintegrasi ke berbagai instansi. Sistem ini dikembangkan oleh Pemerintah Republik Indonesia bernama Sistem Layanan Aspirasi dan Pengaduan Online Rakyat (LAPOR!). Berdasarkan sistem LAPOR jumlah aduan yang masuk terus meningkat. Dengan adanya sistem ini berbagai aduan yang disampaikan oleh warga masyarakat dapat terintegrasi ke instansi yang berwenang menangani aduan tersebut. Dengan terintegrasinya sistem maka jumlah pengaduan yang masuk sangat banyak sehingga terdapat kendala pada saat proses verifikasi pengaduan yang nantinya akan diteruskan ke pihak yang berwenang. Tujuan penelitian ini adalah melakukan klasifikasi terhadap setiap laporan pengaduan dan mengetahui pengaruh terhadap Replace Slang Word. Dalam proses klasifikasi di penelitian ini menggunakan algoritma Artificial Neural Network. Jumlah data pengaduan yang digunakan adalah sebanyak 750 data pengaduan. Data tersebut terbagi menjadi 3 kategori yaitu bidang pendidikan, kesehatan dan infrasturktur. Untuk pembagian jumlah data dilakukan sama di setiap kategori. Pada tahapan Preprocessing menggunakan replace slang word sebagai penggati kata slang terhadap kata aslinya. Hasil dari penelitian ini adalah menunjukkan nilai tinggi terhadap nilai Accuracy yaitu sebesar 99,33% untuk F1 score, Precission, dan Recall memiliki nilai yang sama yaitu 99,09%. Dengan hasil yang tinggi maka metode yang diusulkan dapat digunakan untuk melakukan pengklasifikasian terhadap laporan pengaduan.
Pemanfaatan Sistem Informasi Berbasis Website untuk Mendukung Pengelolaan Administrasi Data Karyawan Yayasan Taruna Alquran Sleman Yogyakarta Nurmasani, Atik; Dyah Anggita, Sharazita; Dwi Hartanto, Anggit; Pujastuti, Eli; Asti Astuti, Ika; Pristyanto, Yoga; Nur Fajri, Ika
Jurnal Pengabdian Masyarakat Inovasi Indonesia Vol 3 No 4 (2025): JPMII - Agustus 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jpmii.829

Abstract

Penerapan sistem informasi pada suatu institusi penting untuk mendukung proses bisnis. Yayasan Taruna Al-Quran ingin memaksimalkan teknologi dalam mengelola administrasi data unit kerja. Masalah yang dialami pada pengelolaan administrasi data yaitu keterbatasan dalam pengelolaan arsip dan tidak optimalnya proses pencarian data. Sistem informasi berbasis website dibuat untuk mengatasi masalah pengelolaan administrasi dan kemudahan akses bagi seluruh unit kerja. Metode yang diterapkan pada kegiatan terdiri dari perencanaan, pelaksanaan, dan evaluasi. Hasil kegiatan perencanaan berupa perencanaan yang sesuai kebutuhan sebagai dasar pelaksanaan.  Hasil kegiatan pelaksanaan berupa sistem informasi yang siap diserahkan kepada mitra. Hasil evaluasi berupa masukan pengguna dari mitra terhadap sistem informasi, dimana pengguna mudah menggunakan sistem informasi dengan skor 5.9 atau 86%. Sistem informasi yang diterapkan dapat membantu mitra mengelola administrasi data karyawan dengan mudah. Seluruh pengguna dapat mengakses data secara online sesuai kebutuhan.
Extreme Gradient Boosting Algorithm to Improve Machine Learning Model Performance on Multiclass Imbalanced Dataset Pristyanto, Yoga; Mukarabiman, Zulfikar; Nugraha, Anggit Ferdita
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1102

Abstract

Unbalanced conditions in the dataset often become a real-world problem, especially in machine learning. Class imbalance in the dataset is a condition where the number of minority classes is much smaller than the majority class, or the number is insufficient. Machine learning models tend to recognize patterns in the majority class more than in the minority class. This problem is one of the most critical challenges in machine learning research, so several methods have been developed to overcome it. However, most of these methods only focus on binary datasets, so few methods still focus on multiclass datasets. Handling unbalanced multiclass is more complex than handling unbalanced binary because it involves more classes than binary class datasets. With these problems, we need an algorithm with features that can support adjustments to the difficulties that arise in multiclass unbalanced datasets. One of the algorithms that have features for adjustment is the ensemble algorithm, namely Xtreme Gradient Boosting. Based on the research, our proposed method with Xtreme Gradient Boosting showed better results than the other classification and ensemble algorithms on eight datasets with five evaluation metrics indicators such as balanced accuracy, the geometric-mean, multiclass area under the curve, true positive rate, and true negative rate. In future research, we suggest combining methods at the data level and Xtreme Gradient Boosting. With the performance increase in Xtreme Gradient Boosting, it can be a solution and reference in the case of handling multiclass imbalanced problems. Besides, we also recommended testing with datasets in the form of categorical and continuous data.
Co-Authors Acihmah Sidauruk Aditya Yoga Pratama Afrig Aminuddin Aisha Shakila Iedwan Akhmad Dahlan Alvin Rahman Al Musyaffa Andi Sunyoto Anggi Thoat Ariyanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anggita, Sharazita Dyah Anna Baita Arif Nur Rohman arif nur rohman Asti Astuti, Ika Atik Nurmasani ATIK NURMASANI Atik Nurmasani Barus, Herianta Bety Wulan Sari Bety Wulan Sari, Bety Wulan Bligania Bligania Cherfly Kaope Donni Prabowo, Donni Dwi Hartanto, Anggit Dyah Anggita, Sharazita Eli Pujastuti, Eli Eza Nanda Fadhilah Dwi Ananda Fajri, Ika Nur Fauzy, Marwan Noor Gagah Gumelar Gita Cahyani Hendra Kurniawan Heri Sismoro Hidayat, Kardilah Rohmat Ibnu Hadi Purwanto Iedwan, Aisha Shakila Ike Verawati Ikmah Ikmah Irfan Pratama Istikomah Khoiruddin, Lukman Kono, Maria Fatima Kristianti, Fanny Novatriana Lucky Adhikrisna Wirasakti Mambaul Hisam Marcheilla Trecya Anindita Mauliza, Nia Mukarabiman, Zulfikar Mulia Sulistiyono Nia Mauliza Nia Mauliza Nugraha, Anggit Ferdita Nuri Cahyono Nurindah A Amari Purwati, Sintia Eka Putra, Frahma Aditya Rahman Saputra, Rahman Rifda Faticha Alfa Aziza Rizky Hafizh Jatmiko Rohmad Fajarudin Rohman, Arif Nur Romadhon, Ibrahim Aji Fajar Rospita, Andri Sabella, Cindy Dinda Sifa’ul Husna, Siti Okta Sumarni Adi Windarni, Vikky Aprelia Wirantanu, Dipa Wirasakti, Lucky Adhikrisna Wiwi Widayani Yanuar Nur Kholik Yudiyanto, Muhammad Resa Arif Yuli Astuti Zein, Aditya Ahmad