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All Journal Jurnal INSYPRO (Information System and Processing) Journal Software, Hardware and Information Technology Journal of Embedded Systems, Security and Intelligent Systems
Mustikasari Mustikasari
UIN Alauddin Makassar
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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ANALISIS CLUSTERING TEKS TANGGAPAN MASYARAKAT DI TWITTER TERHADAP PEMBATASAN SOSIAL BERSKALA BESAR MENGGUNAKAN ALGORITMA K-MEANS Muhammad Nur Akbar; Darmatasia Darmatasia; Mustikasari Mustikasari; Muh Syahwal
Jurnal INSYPRO (Information System and Processing) Vol 6 No 1 (2021)
Publisher : Prodi Sistem Informasi UIN Alauddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (609.636 KB) | DOI: 10.24252/insypro.v6i1.23325

Abstract

Virus corona (COVID-19) ditetapkan sebagai pandemi oleh WHO (World Health Organization atau Badan Kesehatan Dunia) karena penyebarannya yang terus meningkat dan telah mencapai sebagian besar negara di dunia, termasuk Indonesia. Setiap negara dituntut dapat lebih agresif dalam mengambil tindakan pencegahan dan perawatan. Pemerintah Indonesia sendiri mengeluarkan kebijakan berupa wajib masker, jam malam, serta PSBB (Pembatasan Sosial Berskala Besar) guna menekan laju menyebaran COVID-19.  Namun kebijakan tersebut menuai tanggapan  pro dan kontra dari masyarakat khususnya melalui media sosial, di satu sisi PSBB dianggap mampu menekan laju penyebaran COVID-19 namun di sisi lain PSBB dianggap akan memperburuk kondisi perekonomian masyarakat, khususnya golongan menengah bawah. Penelitian ini bertujuan untuk mengelompokkan tanggapan masyarakat mengenai PSBB di twitter ke dalam beberapa cluster, tanggapan yang berada dalam satu cluster yang sama dianggap memiliki topik atau karakteristik pembahasan yang serupa dan sebaliknya, sehingga dapat memberi insight tambahan pada pihak pemerintah dalam mengevaluasi kebijakannya. Algoritma K-Means digunakan untuk mengelompokkan tanggapan yang memiliki kesamaan karakteristik sebab terbukti memiliki tingkat akurasi yang tinggi dengan waktu eksekusi yang relatif cepat karena bersifat linear. Penelitian ini menghasilkan 4 cluster berbeda dengan mengunakan metode Elbow dalam penentuan jumlah K pada algoritma K-Means dan nilai SSE (Sum of Square Error) sebagai parameter evaluasinya.   
Penambangan Pengklasifiksi Fuzzy dengan Multiobjective Evolutionary Fuzzy Classifier Nur Salman; Mustikasari Mustikasari; Muhammad Nur Akbar
Journal Software, Hardware and Information Technology Vol 2 No 1 (2022)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v2i1.24

Abstract

Classification is one of the key issues in the field of data mining and knowledge discovery. This paper implements a method of constructing a fuzzy rule mining classifier, which is extended in the context of classification. There are three stages of this approach: fuzzy rule set extraction, second; a linguistic labeling process that assigns a linguistic label to each fuzzy set. Owing to many attributes in the database, the feature selection process is also carried out, reducing the complexity to build the final classifier. Third: incorporate strategies to avoid rule redundancy and conflict into process mining. We applied the application Multiobjective Evolutionary Fuzzy Classifier (MOFC), which produced a classifier with satisfactory classification accuracy compared to other classifiers such as C4.5. In addition, in terms of classification based on association rules, MOFC can filter the large of rules and be proven to be able to build compact fuzzy models while maintaining a very good level of accuracy and producing a much smaller set of rules. We examine the performance of fuzzy rule classifiers through computational experiments on three benchmark data sets in the UCI machine learning repository.
Analisis Kinerja Algoritma Machine Learning Untuk Deteksi Penyakit Daun Teh Dengan Particle Swarm Optimization Mustikasari; Abdur Rahman Ramli; Andi Khalil Gibran
Journal of Embedded Systems, Security and Intelligent Systems Vol 4, No 2 (2023): November 2023
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v4i2.708

Abstract

Diseases in tea leaves are one of the causes of a decline in the quality and quantity of tea production, appropriate techniques and treatments are needed to detect diseases that can attack tea leaves. This research aims to use the best techniques to help tea farmers detect tea leaf plant diseases early. This research uses Artificial Intelligence-based techniques that apply Machine Learning algorithms to detect diseases in tea leaves. One of the challenges in implementing Machine Learning algorithms is the difficulty of finding parameters that can maximize algorithm performance. In this research, the machine learning algorithms used are Support Vector Machine (SVM) and Gradient Boosting with parameter optimization using Particle Swarm Optimization (PSO) to find the best parameters. There are 5867 images of tea leaves consisting of five types of diseases, namely Algal Spot, Brown Light, Gray Blight, Helopeltis, Red Spot, and healthy leaves used in this research. The research results show that the machine learning algorithm’s performance experienced an increase in accuracy of around 2-4% after being optimized using PSO. The accuracy obtained with the standard SVM was 87%, after optimization, it increased to 91.68%. Meanwhile, the standard Gradient Boosting obtained an accuracy of 89%, and after optimization with PSO, the accuracy increased to 91%. This research is expected to minimize the work of experts and help detect diseases on tea leaves quickly.
Co-Authors Abdur Rahman Ramli Andi Khalil Gibran Darmatasia Darmatasia Hariani Hariani Muh Syahwal Muhammad Nur Akbar Riski Putri Hidayanti Salman, Nur