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All Journal Techno.Com: Jurnal Teknologi Informasi PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta CogITo Smart Journal JTERA (Jurnal Teknologi Rekayasa) Jurnal Sisfokom (Sistem Informasi dan Komputer) KACANEGARA Jurnal Pengabdian pada Masyarakat JSiI (Jurnal Sistem Informasi) JURTEKSI CCIT (Creative Communication and Innovative Technology) Journal Jurnal Pendidikan dan Konseling Jurnal Investasi Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madaniya Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Sekretari & Administrasi (Serasi) Jurnal Teknik Informatika (JUTIF) Teknika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer DECODE: Jurnal Pendidikan Teknologi Informasi Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Telematika MKOM AMMA : Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Tekno INOVTEK Polbeng - Seri Informatika Jurnal Pintar Pengabdian dan Pemberdayaan Masyarakat
Triyono, Gandung
Universitas Budi Luhur
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Journal : JSiI (Jurnal Sistem Informasi)

 1 
PREDICTION MODEL FOR STUDENTS' ON-TIME GRADUATION USING ALGORITHM SUPPORT VECTOR MACHINE (SVM) BASED  PARTICLE SWARM OPTIMIZATION (PSO) Hidayatulloh, Syarif; Gandung Triyono; Kiki Ari Suwandi kosasih
Jurnal Sistem Informasi Vol. 12 No. 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i1.9964

Abstract

One of the indicators in assessing the tridharma of higher education outcomes and achievements is students' timely graduation and one indicator of the success of the higher education system is timely graduation. However, some students cannot complete their studies on time. In the process of completing the study, several problems emerged, one of which was in completing studies on time, there are problems that arise, such as students who are still repeating because there are grades that have not passed in the course, the Grade Point Average (GPA) is still lacking, the Semester Achievement Index (SAI) is still below the minimum, the total number of semester credit units (total credits) which still have not reached the minimum limit, then the number of active lecture statuses still exceeds 8 semesters, so this problem will have an impact on the accuracy of student study graduate data, where the target performance indicator for graduates is on time The student's target of graduating on time has not been achieved. The factors that cause the students not to graduate on time are not known. In identifying the problem, it was found that the Study Program does not have adequate information regarding the potential for students to graduate on time and the limitations of the study program in assisting students in completing on-time graduation The method used to solve this problem is by creating a prediction model for students' on-time graduation, so that students can receive adequate information regarding the potential for graduating on time. This research aims to create a prediction model for graduating on time using the Support Vector Machine (SVM) method based on Particle Swarm Optimization (PSO) with feature selection. information gain, so that the attributes selected and used are Semester Achievement Index 1, Semester Achievement Index 2, Semester Achievement Index 3, Semester Achievement Index 4, Grade Point Average (GPA) 1, Grade Point Average (GPA) 2, Grade Point Average (GPA) 3, Grade Point Average (GPA) 4, Semester Credit Units 1, and Semester Credit Units 4. The results in this study obtained accuracy values ​​of 0.799, precision 0.851, recall 0.605 and AUC 0.86
Klasterisasi Provinsi di Indonesia Berdasarkan Angka Harapan Hidup Menggunakan K-Means dengan Evaluasi Elbow Method Asep Lukman Arip Hidayat; Helmi Zulqan; Gandung Triyono
Jurnal Sistem Informasi Vol. 12 No. 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i1.10116

Abstract

Angka Harapan Hidup (AHH) adalah indikator penting untuk mengukur kesejahteraan masyarakat dan tingkat kesehatan di suatu daerah. AHH Indonesia sangat berbeda antar provinsi, yang menunjukkan perbedaan dalam infrastruktur dan akses layanan kesehatan. Tujuan dari penelitian ini adalah untuk menggunakan algoritma K-Means untuk meklasterisasi provinsi-provinsi di Indonesia berdasarkan AHH, dan untuk mengevaluasi hasil klasterisasi dengan metode Elbow untuk menentukan jumlah klaster yang ideal. Data yang digunakan mencakup AHH dari seluruh provinsi Indonesia dalam beberapa tahun terakhir. Untuk mempermudah identifikasi daerah yang memerlukan perhatian khusus dalam perencanaan kebijakan kesehatan, provinsi diklasifikasikan ke dalam klaster berdasarkan karakteristik AHH yang sebanding. Studi ini fokus pada optimasi Algoritma K-Means dengan metode Elbow. Percobaan iterasi dilakukan sepuluh kali dan menemukan nilai K ideal, yaitu K=3. Hasil klasterisasi menunjukkan bahwa Cluster 0 memiliki 103 anggota, Cluster 1 memiliki 181 anggota, dan Cluster 2 memiliki 260 anggota. Hasil penelitian menunjukkan bahwa algoritma K-Means berhasil mengelompokkan provinsi-provinsi berdasarkan AHH dengan tingkat variasi yang rendah di setiap klaster. Metode Elbow membantu menentukan jumlah klaster yang ideal.
Co-Authors - Sumardianto Abdul Hamid Abdurrahman, Faris Nur Achmad Ardiansyah Achmad Solichin Achmad Syarif Adhi, Ajar Parama Aditya Ikhbal Maulana Agus Umar Hamdani Aji Guntoro Al Ghozali, Isnen Hadi Ananda Dian Nugraha Angga Prasetyo Anggita Pamukti Anggraini Ujianti Anwarsyah, Anwarsyah Asep Lukman Arip Hidayat Assegaf , Noval Chaerul, Muh Coudry Bernadeth Dana Indra Sensuse Daniel Iskandar Dede Wahyu Saputra Dermawan Ginting Devy Fatmawati Dini Astuti Dini Handayani, Dini Djafar, Muhammad Agung A. Djati Kusdiarto Dolly Virgian Shaka Yudha Sakti Dwi Kristanto Dyah Puji Utami Effendi , Muhtar Eliyani, Eliyani Ery Rinaldi Fachrurozy, Achmad Fadel, Muhamad Fahlevi, Noval Fajriah, Riri Febri Maulana Feby Lukito Wibowo Firmansyah, Maulana Gilang Ramadhan Hadi rahadian Hafiz, Rahmad Hakim, Sulaiman Hanifa, Annisa Hardjianto, Mardi Helmi Zulqan Hendra Adi Saputra Henny Idam Risnaputra Iman Permana, Iman Indra Indra Jotri Firdani Maharaja Juhari Juhari, Juhari Jumaryadi, Yuwan Kanasfi, Kanasfi Kiki Ari Suwandi kosasih Lestari, Triardani Lis Suryadi Lis Suryadi, Lis Lutfan Lazuardi Luthfi Mawardi Mahendra, M. Azmi Malik Aziz Habibie Maruanaya, Greghar Juan Tjether Maruanaya, Rita Fransina Maskur A, Moch Riyadi Masnuryatie, Masnuryatie Maya Asmita Megananda Hervita P. Melyana, Melyana Mepa Kurniasih MHD. Reza M.I. Pulungan Moch. Rezaf Ivanka Haris Mohammad Aldinugroho Abdullah Muhamad Dikhi Rohman Muttaqin, Zaenul Nita, Yulia Oktiara, Dara Putri Pebry, Fachry Ajiyanda Pirman, Arif Prasetia, Andika Rohman Prasetyo, Angger Totik Rahmat Hidayat Reza Ariftiarno Ridho Firmansyah Ridho Putra Kusmanda Riki Ramdani Saputra Rima Tamara Aldisa Rinto Prasetyo Adi Rizka Pitriyani Rizky Adhi Saputra Rizky Fernanda Aprianto Rizky Tahara Shita Rojakul, Rojakul Rudi Hartono Rudi Hidayat Ryan Prasetya Safrina Amini Septiadi, Septiadi Setyadin, Rahmat Dipo Sister, Maya Gian Sittah Ifadah Sri Hartati Sri Melati Subekti, Yogi Agung Sudiyatno Yudi Nugroho Sufyan Asaury, Akhmad Suriah Setiana Widiastuti SURYANI Syarif Hidayatulloh Tansya Ingmukti Taryono, Ono Tunggal Saputra, Tri Aji Umar Alfaruq Umuri, Khairil Utomo Budiyanto Vasthu Imaniar Ivanoti Wahyu Adi Setyo Wibowo Wahyu Cesar, Wahyu Wahyuningram, Nugroho Warih Dwi Cahyo Wawan Gunawan Widyanto, Tetrian Wilsen Grivin Mokodaser Winasis, Reza Handaru Wisanto, Aditya Agus Wisnu Cahyadi Wulan Trisnawati Yasmin , Nadia Yeros Fathullah Achmad Zainal Arifin