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S. Kuba, Muhammad Syafa'at
Universitas Muhammadiyah Makassar
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Transportation Other

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IMPLEMENTASI HYBRID LEXICON-BASED DAN SVM UNTUK KLASIFIKASI ANALISIS SENTIMEN TERHADAP PELATIHAN BBPSDMP KOMINFO MAKASSAR Alam, Nur; Faisal, Muhammad; Bakti, Rizki Yusliana; Syafaat, Muhammad; Syamsuri, Andi Makbul; AM Hayat, Muhyiddin; Anas, Andi Lukman
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i2.473

Abstract

The evaluation of government training programs is often hindered by manual analysis of unstructured qualitative feedback, making the process inefficient and subjective. This study aims to implement and evaluate a sentiment classification model using a hybrid Lexicon-Based and Support Vector Machine approach to analyze participants’ perceptions of the Vocational School Graduate Academy training organized by BBPSDMP Kominfo Makassar, as well as to compare the performance of a standard SVM model with a model optimized using Particle Swarm Optimization. This quantitative research employs 2,313 unstructured review data, which undergo text preprocessing, initial lexicon-based labeling, and TF-IDF feature extraction before being classified using an SVM with an RBF kernel. The results show that the SVM model optimized with PSO consistently outperforms the standard model across all four evaluation aspects, with the most significant accuracy improvement observed in the instructor category from 84.71% to 89.02% and in the assessor category reaching 91.46%. PSO optimization has proven effective in enhancing the model’s ability to identify negative sentiments, which represent the minority class. The hybrid approach with PSO optimization is capable of producing a more accurate and balanced classification system, with practical implications as an objective automated evaluation tool.
PREDIKSI CURAH HUJAN WILAYAH MENGGUNAKAN METODE JARINGAN SARAF TIRUAN (JST) BACKPROPAGATION DI DAS RONGKONG Syahrul, Syahrulrahman; T Karim, Nenny; S Kuba, Syafaat
BANDAR: JOURNAL OF CIVIL ENGINEERING Vol 5 No 2 (2023): Bandar: Journal of Civil Engineering
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/bjce.v5i2.2945

Abstract

Hidrologi merupakan salah satu faktor yang diperlukan untuk penyelesaian suatu permasalahan sumber daya air. Analisa curah hujan sangatlah diperlukan sebelum melakukan suatu prediksi. DAS Rongkong seluas 1728,034 Km²yang terletak di Kabupaten Luwu Utara mengalami permasalahan tentang peralihan tata guna lahan yang menyebabkan kemampuan infiltrasinya menjadi berkurang. Tujuan dari penelitian ini adalah untuk prediksi curah hujan di DAS Rongkong dan mengetahui perbandingan antara data lapangan dengan data hasil prediksi menggunakan Metode Jaringan Syaraf Tiruan (JST) Back Propagation. Berdasarkan hasil penelitian diperoleh hasil pada tahun 2022 bulan Januari: 66 mm, Februari : 25 mm, Maret : 22 mm, April : 19 mm, Mei : 22 mm, Juni : 40 mm, Juli : 18 mm, Agustus : 18 mm, September : 66 mm, Oktober : 18 mm, November : 30 mm, Desember : 67 mm. Berdasarkan Grafik data lapangan dengan data hasil prediksi Metode JST Pada bulan Januari, September dan Desember curah hujan wilayah yang dihasilkam mengalami kenaikan ,Sedangkan Pada bulan Februari, Maret, April, Mei, Juni, Juli, Agustus dan November curah hujan wilayah yang dihasuilkan mengalami penurunan. Sehingga Rata-rata selisih yang dihasilkan sebesar 13,83 mm.
Co-Authors Agusalim Agusalim Agusalim, M. Ahmad Syafi'i Zulmi Akbar, Syahril Akrar Syah Ali, Muhammad Yunus AM Hayat, Muhyiddin Amal, Citra Amalia Anas, Andi Lukman Anas, Lukman Andi Bunga Tongeng Anas Andi Rahmat Anis Dandi Juandani Bakti, Rizki Yusliana Berni Satria Gemilang Djunur, Lutfi Hair Faeruddin, Muhammad Asygar Fausiah Latief Fauzan Hamdi Hamzah Al Imran Hayat, Muhyiddin A M Irma Suryana Irwan Lisnawati Lisnawati Lukman Lukman Lutfi Hair Djunur M. Agusalim Mahmuddin Mahmuddin Mahmuddin Mohamad Munawir Muh Alvin Achmad Kato Muhammad Faisal Mujidah, Jihan Izzathul Nenny Nenny Nini Apriani Rumata Nur Alam Nurnawaty Nurnawaty Panguriseng, Darwis Putri, Adriani Rahmasari, St. Rajib Mahmud Risman, Andi Muh. Riswal Karamma Sahril Sandi, Andi Muhammad Sarina Syahrul, Syahrulrahman Syamsuri, Andi Makbul Syarifuddin, Nur Annisa T Karim, Nenny Titin Wahyuni Toha Andi Lala Usman, Sucipto Zulfikar Arsyad