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All Journal Applied Information System and Management Jurnal Tekno Kompak Jurnal Data Mining dan Sistem Informasi
Syahirul Alim
Universitas Teknokrat Indonesia
Computer Science & IT Languange, Linguistic, Communication & Media

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Study The SVM Kernel For Classification Of Covid-19 Vaccine Data On Twitter Styawati Styawati; Andi Nurkholis; Syahirul Alim; Nadiya Safitri
Jurnal Tekno Kompak Vol 17, No 1 (2023): Februari
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v17i1.2254

Abstract

The rapid development of Covid-19 in Indonesia in 2020 caused the government too blige all Indonesian people to carry out the Covid-19 vaccination. The public's response to the policy, some agree and some disagree. The response is widely pouredon social media, one of whichis Twitter. Social media Twitter is ranked 5th in the category of the most used social media with a user percentage of 56%. This shows that there is a very large opportunity for data sources that can be used to find out positive and negative public sentiment regarding the Indonesian government's policy regarding Covid-19 vaccination. The method used to classify in this research is Support Vector Machine with various kernels. The kernels used are Linear, RBF, Polynomial, and Sigmoid. The classification results using the kernel are that the RBF kernel produces an accuracy of 88.8%, the Linear kernel produces an accuracy of 88.3%, the Sigmoid kernel produces an accuracy of 87% and the Polynomial kernel produces an accuracy of 85.5%. Based on the classification process that has been carried out, the highest accuracy is generated by the RBF kernel and the lowest accuracy is generated by the Polynomial kernel.
SISTEM PAKAR DIAGNOSA PENYAKIT TANAMAN KAKAO MENGGUNAKAN METODE CERTAINTY FACTOR PADA KELOMPOK TANI PT OLAM INDONESIA (COCOA) CABANG LAMPUNG Syahirul Alim; Peni Puji Lestari; Rusliyawati Rusliyawati
Jurnal Data Mining dan Sistem Informasi Vol 1, No 1 (2020): Agustus 2020
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v1i1.798

Abstract

PT OLAM INDONESIA (Cocoa) Cabang Lampung memiliki petani binaan sebanyak 2700. Ada beragam jenis penyakit yang menyerang tanaman kakao. Jenis penyakit yang sering menyerang tanaman kakao yaitu penyakit busuk buah, penyakit kanker batang, penyakit antraknosa, penyakit jamur upas, penyakit jamur akar, penyakit pembuluh kayu (Vascular Steak Dieback). Permasalahannya adalah dikarenakan banyaknya petani binaan olam yang tidak mengetahui jenis penyakit dan solusi. Berdasarkan masalah tersebut, maka peneliti akan merancang sebuah sistem pakar diagnosa penyakit tanaman kakao menggunakan metode certainty factor pada kelompok tani pt olam indonesia (cocoa) cabang lampung. Sistem yang dibangun adalah sistem yang dapat mendiagnosa penyakit tanaman kakao serta memberikan solusi yang tepat terhadap penyakit yang sering menyusahkan para petani kakao. Hasil akhir yang diperoleh adalah sistem informasi diagnosa penyakit tanaman kakao menggunakan metode certainty factor yang memiliki kemampuan untuk mengetahui jenis penyakit sesuai dengan yang dikeluhan petani. Aplikasi ini diuji menggunakan perhitungan akurasi dimana hasilnya didapatkan tingkat akurasi sebesar 85,7% untuk diagnose penyakit tanaman kakao.
APLIKASI CUSTOMER RELATIONSHIP MANAGEMENT MENGGUNAKAN PENDEKATAN FRAMEWORK OF DYNAMIC (STUDI KASUS: PT BINTANG KHARISMA MOTOR BANDAR LAMPUNG) Syahirul Alim
Jurnal Data Mining dan Sistem Informasi Vol 2, No 1 (2021): Februari 2021
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v2i1.1022

Abstract

PT Bintang Kharisma Motor is a Honda dealer engaged in motorbike service located on Jalan Raya Soekarno Hatta 5A Tanjung Seneng, Bandar Lampung. PT Bintang Kharisma Motor has equipment related to motorbike service, which does not have a system for ordering motorbike service orders and there is no system to measure the level of service found at PT Bintang Kharisma Motor. Apart from that, the recording of the purchase and sale of spare parts still uses manual recording so that distorting data on the purchase and sale of spare parts is still deemed inefficient and ineffective. Making reports that are long and deemed ineffective makes researchers want to develop a system at PT Bintang Kharisma Motor. In making the system at PT Bintang Kharisma Motor made with UML diagrams consisting of three diagrams, namely usecase diagrams, activity diagrams, and class diagrams as the initial system design to be made, then programming is made with the PHP programming language according to the existing user interface design. and MySql as database. The system that has been created can help distort sales / service data, purchase and create purchase and sales reports faster than before, and the system helps in processing complaint data, ordering services, and measuring the level of service provided at PT Bintang Kharisma Motor.
Sentiment Analysis of COVID-19 Booster Vaccines on Twitter Using Multi-Class Support Vector Machine Andi Nurkholis; Styawati Styawati; Syahirul Alim; Hendi Saputra; Andrey Ferriyan
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.42911

Abstract

The Indonesian government's implementation of a booster vaccination program as part of its COVID-19 response has generated diverse public reactions, particularly on social media platforms like Twitter. This study aims to analyze public sentiment regarding booster vaccines by examining Twitter data to understand the prevailing discourse and attitudes toward this policy. The research employs sentiment analysis, a text mining and processing technique, to classify tweets into positive, neutral, and negative categories. The study utilizes the Support Vector Machine (SVM) algorithm, evaluating its performance through a multi-class parameter assessment. Two multi-class strategies, One-against-one (OAO) and One-against-rest (OAR) are combined with various kernels (Sigmoid, Polynomial, and RBF) to identify the most accurate model for sentiment classification. The results show that the OAO method with the RBF kernel achieves the highest accuracy of 96%, outperforming other combinations like OAO with Polynomial (95.2%) and Sigmoid (93.7%) kernels. Similarly, the RBF kernel performs best with 95.5% accuracy in the OAR approach. Using the optimal model, sentiment analysis classifies 49 tweets as positive, 927 as neutral, and 24 as negative, revealing a predominantly neutral public sentiment with limited positive and negative opinions. In conclusion, this study demonstrates the effectiveness of SVM, particularly the OAO method with the RBF kernel, for sentiment analysis of social media data. The findings provide insights into public perceptions of the booster vaccine program, offering policymakers a data-driven basis for designing targeted communication strategies to address concerns and enhance public acceptance.
Co-Authors ., Rusliyawati Andi Nurkholis Andi Nurkholis Andrey Ferriyan Hendi Saputra Nadiya Safitri Peni Puji Lestari Styawati Styawati