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Journal : JURTEKSI

APPLICATION OF PARTICLE SWARM OPTIMIZATION SUPPORT VECTOR MACHINE FOR ELECTRICAL INSTALLATION CERTIFICATION PREDICTION Priyono, Priyono; Panca Saputra, Elin; Suswandi, Suswandi; Rahman, Taufik
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 2 (2025): Maret 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i2.3418

Abstract

Abstract: Feature selection is a crucial process that is very important to improve the performance of machine learning models, in accordance with data preprocessing. The feature selection process can be considered as a global combinatorial optimization problem in machine learning, which reduces the number of features, eliminates irrelevant data, and produces acceptable classification accuracy. The purpose of this study is to predict or determine the results of the electrical installation operation feasibility test based on data and obtain attribute selection features, and obtain accuracy level results. The Particle Swarm Optimization (PSO) approach is used to select the right characteristics to determine the results of the electrical installation operation feasibility test because attribute selection is needed in data analysis, because the PSO method will increase accuracy than just SVM in determining attribute selection. If SVM is used with PSO, the accuracy value is 96% and AUC is 0.994%, while the SVM method produces an accuracy level of 94.89% and AUC of 0.994%. With this finding, the accuracy value increases by 2%, making it a very good categorization category. It has been proven that the use of Particle Swarm Optimization (PSO) based algorithms can improve and improve results.            Keywords: PSO; SVM; Certification Abstrak: Pemilihan fitur merupakan proses krusial yang sangat penting untuk meningkatkan kinerja model machine learning, sesuai dengan praproses data. Proses pemilihan fitur dapat dianggap sebagai masalah optimasi kombinatorial global dalam pembelajaran mesin, yang mengurangi jumlah fitur, menghilangkan data yang tidak relevan, dan menghasilkan akurasi klasifikasi yang dapat diterima. Tujuan dari penelitian ini adalah untuk memprediksi atau menentukan hasil uji kelayakan operasi instalasi listrik berdasarkan data dan memperoleh fitur pemilihan atribut, serta memperoleh hasil tingkat akurasi. Pendekatan Particle Swarm Optimization (PSO) digunakan untuk memilih karakteristik yang tepat untuk menentukan hasil uji kelayakan operasi instalasi listrik karena pemilihan atribut diperlukan dalam analisis data, karena metode PSO akan meningkatkan akurasi dari pada hanya SVM dalam menentukan pemilihan atribut. Jika SVM digunakan dengan PSO, nilai akurasinya adalah 96% dan AUC sebesar 0,994%, sedangkan metode SVM menghasilkan tingkat akurasi sebesar 94,89% dan AUC sebesar 0,994%. Dengan temuan ini, nilai akurasi meningkat sebesar 2%, menjadikannya kategori kategorisasi yang sangat baik. Telah terbukti bahwa penggunaan algoritma berbasis Particle Swarm Optimization (PSO) dapat meningkatkan dan memperbaiki hasil. Kata kunci: PSO; SVM; Sertifikasi 
ANALYSIS OF THE QUALITY OF "ONLINE EQUIVALENT" E-LEARNING USING WEBQUAL 4.0 AND IPA METHODS Rahman, Taufik; Azizah, Alfi
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i4.3792

Abstract

Abstract: The use of e-learning in non-formal education is increasingly important to support the improvement of access to learning, one of which is through the online platform. This study aims to analyze the quality of online services using WebQual 4.0 and Im-portance Performance Analysis (IPA) methods to evaluate the suitability between user expectations and perceptions. The research method used a quantitative approach by distributing questionnaires to active users, then analyzed using the WebQual Index to measure the overall quality of the system as well as the IPA to determine improvement priorities. The results showed that the quality of SeTARA Online was relatively good with a WebQual Index value of 0.798. However, there is still a gap between user expectations and satisfaction with a negative gap value of -0.238. The IPA analysis identified indicators in Quadrant I as priority improvements, especially in the aspects of service interaction and information presentation. These findings underscore the need for continuous development of features and technical support to optimize the user experience. The conclusion of this study suggests that there should be improvements in priority indicators to increase user satisfaction, as well as strengthen the effectiveness of online learning. Advanced research can expand variables, compare with other platforms, and combine quantitative and qualitative analysis methods for more comprehensive results. Keywords: e-learning; importance performance analysis; quality of service; online equivalent; webqual 4.0
APPLICATION OF PARTICLE SWARM OPTIMIZATION SUPPORT VECTOR MACHINE FOR ELECTRICAL INSTALLATION CERTIFICATION PREDICTION Priyono, Priyono; Panca Saputra, Elin; Suswandi, Suswandi; Rahman, Taufik
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 2 (2025): Maret 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i2.3418

Abstract

Abstract: Feature selection is a crucial process that is very important to improve the performance of machine learning models, in accordance with data preprocessing. The feature selection process can be considered as a global combinatorial optimization problem in machine learning, which reduces the number of features, eliminates irrelevant data, and produces acceptable classification accuracy. The purpose of this study is to predict or determine the results of the electrical installation operation feasibility test based on data and obtain attribute selection features, and obtain accuracy level results. The Particle Swarm Optimization (PSO) approach is used to select the right characteristics to determine the results of the electrical installation operation feasibility test because attribute selection is needed in data analysis, because the PSO method will increase accuracy than just SVM in determining attribute selection. If SVM is used with PSO, the accuracy value is 96% and AUC is 0.994%, while the SVM method produces an accuracy level of 94.89% and AUC of 0.994%. With this finding, the accuracy value increases by 2%, making it a very good categorization category. It has been proven that the use of Particle Swarm Optimization (PSO) based algorithms can improve and improve results.            Keywords: PSO; SVM; Certification Abstrak: Pemilihan fitur merupakan proses krusial yang sangat penting untuk meningkatkan kinerja model machine learning, sesuai dengan praproses data. Proses pemilihan fitur dapat dianggap sebagai masalah optimasi kombinatorial global dalam pembelajaran mesin, yang mengurangi jumlah fitur, menghilangkan data yang tidak relevan, dan menghasilkan akurasi klasifikasi yang dapat diterima. Tujuan dari penelitian ini adalah untuk memprediksi atau menentukan hasil uji kelayakan operasi instalasi listrik berdasarkan data dan memperoleh fitur pemilihan atribut, serta memperoleh hasil tingkat akurasi. Pendekatan Particle Swarm Optimization (PSO) digunakan untuk memilih karakteristik yang tepat untuk menentukan hasil uji kelayakan operasi instalasi listrik karena pemilihan atribut diperlukan dalam analisis data, karena metode PSO akan meningkatkan akurasi dari pada hanya SVM dalam menentukan pemilihan atribut. Jika SVM digunakan dengan PSO, nilai akurasinya adalah 96% dan AUC sebesar 0,994%, sedangkan metode SVM menghasilkan tingkat akurasi sebesar 94,89% dan AUC sebesar 0,994%. Dengan temuan ini, nilai akurasi meningkat sebesar 2%, menjadikannya kategori kategorisasi yang sangat baik. Telah terbukti bahwa penggunaan algoritma berbasis Particle Swarm Optimization (PSO) dapat meningkatkan dan memperbaiki hasil. Kata kunci: PSO; SVM; Sertifikasi 
Co-Authors Abdul Gofur Abdul Rofik, Abdul Abdurrohman, Irwan Ade Nurman Saputra Adi Rahmat Adyan, M. Audi Afrianto, Afrianto Ahmad Sahroni, Abdul ahmad yani Akmali, Raga Ziqri Alfan Khudori Alfi Azizah Amalia, Olinda Rizky Amka, Amka Ananda Bagus Pramastya Andi Andrean Maulana Putra Anggara, Deffri Anisah Indriati, Anisah Annie Satriani Achwani Aprianto, Qori Ariyandi Batubara, Ariyandi Aryo Tunjung Kusumo atik suprapti Aulya, Nur Hafizatun Awati Darmana, Feni Ayu Citra Dewi Ayuni, Baiq Annisa Sulistia Azizah, Ghina Buya Ibrahim Cahyadi, Septian Chairudin, Muhammad Rio Cita Tresnawati, Cita Dahlia Fisher Damayanti, Anggia Fitri Dani Mardiyana Dedy Kurniawan Dewi Imelda Roesma Djoa, Dominikus Djago Dwi Feriyanto Ersu, Aulia Padhila Ervian Dwi Anggara Eryani, Trisna Waty Riza Fajri Hidayat, Fajri Farach, Nurul Fathimatuzzahra, Qeisha Fathurrahman Fathurrahman Fatwa Aulia Rahman Fauzania, Salsa Fauziah, Karlita Vaulin Fauziah, Karyna Febrianti, Mariska Febriyani, Wulida Khomsah Felix Wuryo Handono Fitriah, Afifah Fitriana Rahmawati Fitriya, Fitriya Frastya, Ayu Linda Giatika Chrisnawati Giovanni Maria Vianney Tobia Mariatmojo Gustika, Fina Gustin, Nesha Asmilia H. A., Haliza Amera Hadi Candra, Tri Hafis Nurdin Hana Tasyia, Nona Hasan Hariri Hasanah Hayati, Annida Putri Helmi, Haidar Herman Kuswanto Herman Kuswanto Herman Kuswanto, Herman Hermawan, Vevi Ichtiari, Atina Rahmah Ida Ayu Oka Suwati Sideman Idris, Fahmi Ihsan, Dzul Irsyadul Fikri Ihsan, Saepul Irfan Maulana Islam, M. Syahid Ismawati, Tuti Jayawarsa, A.A. Ketut Jeki Saputra Jufri Jufri Juliwis Kardi Jusep Saputra, Jusep Karimi, Amir Kartini, Srikandi Ayu Khairunisa, Fadia Khairunnisa, Zahra Syafira Khofani, Tanfidia Eka Kusnadi Kusuma, Rendra Kusumo, Aryo Tunjung Lailiya, Farhah Lantun Paradhita Dewanti Leny Rudihartati Lestari, Wiwit Yuli Lilit Rusyati Lingga Wijaya, Harma Oktafia Maharani, Dhea Tinde Fitriana Maharani, Ega Adinda Putri Mahmudi, Muhammad Zaki Mairawita Mairawita, Mairawita Marliani, Leni Maswinda, Maswinda Maulida, Azalia Mawadatul Maulidah Mawaddah, Putri Afifah Mayasti, Nur Kartika Indah Mazrur, Mazrur Meli Meli, Meli Mildawati Mohamad Erihadiana, Mohamad Mr Sjaeful Anwar Muarif, M. Ari Mubiar Agustin Muhammad Ferdiansyah Muhammad Hasan Basri muhammad qomaruddin Muhammad Qomaruddin Muhammad Ulin Nuha Mukhnil, Nur Fitriya Isnaini Muslim Muslim Muzammil, Azriel Ahmad Nadiyah Hidayati Nahdiyati, Kartika Nibras, Iqbaluz Zain Noviana, Febi Nur Alfian, Calvin Nur Aminudin Nur Fatimah Nuraisyah, Winda Nur’Azizah, Yuni Nurhayani nurhayani Nurjhani, Mimin Nurlaila, Mila Oei, Selviana Oktoviyana, Winstin Pamungkas, Evan Tri Panca Saputra, Elin Parsaoran Siahaan Pertiwi, Lola Pramastya, Ananda Bagus Pringgo Kusuma Dwi Noor Yadi Putra Priyono Priyono Rahmah, Eirvina Rahmawaty, Dede Ayu Ramadani, Octavia Nila Riandi Riandi Riandi Ridha, Fabrobi Ridwan, Muhammad K. Rifqy Muzhaky Adha rofiqi, budiyana Rosidah, Aimmatur Rozen, Ilham Safrina, Lina Salman Alfarisi Salimu Salmiah, Baiq Himayatussifa Sapraji, Sapraji Saputro, Achmat Yulyadi Sartika, Diana Satrianansyah, Satrianansyah Senjani, Ratu Shafara, Dona Sholatiah, Lathifah Shulkhah, Shulkhah Sihombing, Rizky Agassy Simamora, Ellyne Addelyna Siregar, Shopiah Dhuha Siti Khudaifanny Dasa Febrianti Asna Putri Siti Nurajizah, Siti siti, sitirodiyahawaliyah Sobari, Irwan Agus Sofronia, Corazon Solfiyeni Solfiyeni Solihat, Ihat Stelladarifa, Laurent Elizabeth Subhi, M. Suhada, Wiwin Sukamto, Anton Sulaiman, Zaid Sumarna Sumarna Sumarna Sumarna Sunyono - - Suriati, Suriati Surtikanti Hertien Koosbandiah, Surtikanti Suswandi, Suswandi Suwandi, Tri Syachrofi, Muhammad Syaiful Bachri Syayyadi, Achmad Syifa Az Zahra S’adah, Rizki Teguh Rahmat Zaini Thaibah, Hayatun Tri Nurhayati, Maria Nelly Ulzanah, Sifa Vera Pujani Wahyuni, Dinni Wardoyo, Rifqi Choiri Wicaksono, Garda Arif Yassierli Yassierli, Yassierli Yatim, Siti Ainor Mohd Yomi Romlah, Oom Yudianor, Zahwa Ramadhani Zahidah Zahidah, Zahidah Zanah, Nurul Fatihal Zulfikri