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All Journal Jurnal Sains dan Teknologi Jurnas Nasional Teknologi dan Sistem Informasi CogITo Smart Journal Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah DoubleClick : Journal of Computer and Information Technology MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Scientific Journal of Informatics JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) IJIIS: International Journal of Informatics and Information Systems JPMB: Jurnal Pemberdayaan Masyarakat Berkarakter Journal of Computer Networks, Architecture and High Performance Computing Teknika Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat TIERS Information Technology Journal Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Jurnal Pengabdian Mitra Masyarakat (JPMM) Jurnal Krisnadana Prosiding Seminar Nasional Pemberdayaan Masyarakat (SENDAMAS)
Pungkas Subarkah
Universitas Amikom Purwokerto
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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COMPARISON OF SVM AND NAÏVE BAYES CLASSIFIER ALGORITHMS ON STUDENT INTEREST IN JOINING MSIB Amira Aida Rashifa; Hendra Marcos; Pungkas Subarkah; Siti Alvi Sholikhatin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5270

Abstract

Machine learning (ML) is a branch of artificial intelligence (AI) that deals with the development of systems capable of learning from data to make predictions or decisions without being explicitly programmed. In this study, we conducted an analysis of students' interest in the Internship and Certified Independent Study Program (MSIB) in the context of the Independent Campus Learning policy. The method used is a survey by distributing questionnaires to students of Amikom Purwokerto University in the MSIB batch 5 in year 2023. The results of this study can provide understanding and predictions about students' interest in the MSIB program based on relevant variables, such as study program, semester, cumulative grade point average (GPA), semester credit system (SKS), and previous work experience. The research results indicate that GPA and Study Program greatly influence students' interest in MSIB. The Naïve Bayes algorithm yielded an accuracy of 0.6875 on the training data and 0.25 on the testing data, with a confusion matrix of (0, 1, 0; 0, 1, 2; 0, 0, 0). Meanwhile, the Support Vector Machine (SVM) algorithm yielded an accuracy of 0.4375 on the training data and 0.75 on the testing data, with a confusion matrix of (0, 1; 0, 3). The machine learning model developed in this study is expected to help predict students interest based on new data provided, thus supporting decision-making in optimizing the MSIB program.
SENTIMENT ANALYSIS ON RENEWABLE ENERGY ELECTRIC USING SUPPORT VECTOR MACHINE (SVM) BASED OPTIMIZATION Pungkas Subarkah; Bagus Adhi Kusuma; Primandani Arsi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5575

Abstract

Government policy regarding the discourse on the use of renewable energy in electricity, this discourse is widely discussed in the community, especially on social media twitter. The public's response to the implementation of the use of renewable energy varies, there are positive, negative and neutral responses to this government policy. Sentiment analysis is part of Machine Learning which aims to identify responses in the form of text. The data used in this study amounted to 1,367 tweets. The purpose of this study is to determine the sentiment analysis of government discourse related to the use of renewable energy using an optimisation-based Support Vector Machine (SVM) algorithm approach. This research involves several stages including data collection, data pre-processing, experiments and modelling and evaluation. The data is divided into 3 classes, 120 positive, 1221 neutral and 26 negative. In this research, there are five optimisation models used namely Forward Selection, Backward Elimination, Optimised Selection, Bagging and AdaBoost. The results obtained are the use of Optimised Selection (OS) optimisation with the Support Vector Machine (SVM) algorithm obtained an increase in accuracy from 93% to 96%. The increase in the use of SVM using selection optimization obtained the highest increase, because other optimization techniques only reached 1% and 2% of the original results using the SVM algorithm, namely the accuracy value of 93% to 96% (high accuracy). From the research that has been done, it is certainly important to understand public sentiment towards renewable energy policies, especially renewable energy electricity, the hope is that this research will become a reference for the government.
Comparison of Naive Bayes and SVM in Public Opinion Sentiment Analysis on Platform X Salma Ngarifatul Khofiyah; Pungkas Subarkah
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 10 No. 2 (2025)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v10i2.478

Abstract

The growth of social media has made it the primary means for the general public to express their opinions, including on political and legal issues in Indonesia. One topic that has been widely discussed is the abolition of Tom Lembong and the amnesty granted to Hasto Kristiyanto by President Prabowo Subianto, which has garnered mixed public reactions on the X platform. The purpose of this study is to analyze public sentiment regarding current issues and compare the performance of two machine learning algorithms, Naïve Bayes and Support Vector Machine (SVM), to classify public opinion. Data was obtained through a crawling process of 3,003 tweets, followed by a preprocessing stage that included cleaning, case folding, slang normalization, tokenizing, stopword removal, and stemming. Next, a suitability analysis using the TF-IDF method was conducted before the data was processed by the two algorithms. The results showed that, of the 2,998 valid tweets, 78.6% of public opinion was negative and only 21.4% was positive, indicating a predominance of criticism of the issues discussed. When comparing the algorithms, SVM provided more accurate results with an accuracy rate of 78.66%, while Naïve Bayes only achieved 58%. This shows that SVM is more flexible in analyzing text data with a high level of complexity compared to Naïve Bayes.
Co-Authors A. Kholil Hidayat Abdallah, Muhammad Marshal Adam Prayogo Kuncoro Adam Prayogo Kuncoro Akhmad Mustolih Ali Nur Ikhsan Alya Khansa Dzakkiyah Amira Aida Rashifa Anggi Tri Dewi Septiani Anunggilarso, Luky Rafi Arbangi Puput Sabaniyah Azizan Nurhakim Bagus Adhi Kusuma Bryan Jerremia Katiandhago Budi Utami, Dias Ayu Chendri Irawan Satrio Nugroho Chyntia Raras Ajeng Widiawati Cindy Magnolia Dava Patria Utama Dewi Fortuna Dias Ayu Budi Utami Didit Suhartono Dwi Putra, Ruly Niko Enggar Pri Pambudi Fandy Setyo Utomo Faridatun Nida Fiby Nur Afiana Gina Cahya Utami Harun Alrasyid Hendra Marcos Hidayah, Debby Ummul Ikhsan, Ali Nur Irfan Santiko Irma Darmayanti Isnaini, Khairunnisak Nur Isnaini, Khairunnisak Nur Jali Suhaman Latifah Adi Triana Luki Rafi Anuggilarso Maharani Kusuma Dewi Mohd Sanusi Azmi Muhammad Marshal Abdallah Muhammad Ma’ruf Muhammad Rifqi Anshari Nanda Nurisya Merliani Nandang Hermanto Neta Tri Widiawati Nikmah Trinarsih Nur Hidayah, Septi Oktaviani Nur Isnaeni Khoerida Prastyadi Wibawa Rahayu Primandani Arsi Ragil Wilujeng Rayinda Maya Anjani Reza Aditya Permana Reza Aditya Permana Reza Arief Firmanda Riyanto Riyanto Riyanto Riyanto Rizki Sadewo Rosana Fadilla Sari Rujianto Eko Saputro Salma Ngarifatul Khofiyah Sekhudin Sekhudin Septi Oktaviani Nur Hidayah Septi Oktaviani Nur Hidayah Sholikhatin, Siti Alvi SITI ALVI SHOLIKHATIN Siti Alvi Solikhatin Siti Alvi Solikhatin Suhaman, Jali Trian Damai Tripustikasari, Eka Tripustikasari Wachyu Dwi Susanto Wanda Fitrianingsih Wenti Risma Damayanti Wenti Risma Damayanti Yuli Purwati