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All Journal JURNAL SISTEM INFORMASI BISNIS Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Riau Journal of Computer Science Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Indonesian Journal of Artificial Intelligence and Data Mining Rang Teknik Journal Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Technology and Computer Engineering Jambura Journal of Informatics ComTech: Computer, Mathematics and Engineering Applications Systematics Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Applied Engineering and Technological Science (JAETS) JUKI : Jurnal Komputer dan Informatika Jurnal Perangkat Lunak Login : Jurnal Teknologi Komputer Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Jurnal Administrasi Sosial dan Humaniora (JASIORA) Jurnal Informatika Ekonomi Bisnis RJOCS (Riau Journal of Computer Science)
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
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Journal : International Journal of Artificial Intelligence Research

 1 
Determination of Student Subjects in Higher Education Using Hybrid Data Mining Method with the K-Means Algorithm and FP Growth Larissa Navia Rani; Sarjon Defit; L. J. Muhammad
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (379.161 KB) | DOI: 10.29099/ijair.v5i1.223

Abstract

The large number of courses offered in an educational institution raises new problems related to the selection of specialization courses. Students experience difficulties and confusion in determining the course to be taken when compiling the study plan card. The purpose of this study was to cluster student value data. Then the values that have been grouped are seen in the pattern (pattern) of the appearance of the data based on the values they got previously so that students can later use the results of the patterning as a guideline for taking what skill courses in the next semester. The method used in this research is the K-Means and FP-Growth methods. The results of this rule can provide input to students or academic supervisors when compiling student study plan cards. Lecturers and students can analyze the right specialization subject by following the pattern given. This study produces a pattern that shows that the specialization course with the theme of business information systems is more followed by students than the other 2 themes
Prediction of Scholarship Recipients Using Hybrid Data Mining Method with Combination of K-Means and C4.5 Algorithms Mardison Mardison; Sarjon Defit; Shaza Alturky
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (408.759 KB) | DOI: 10.29099/ijair.v5i2.224

Abstract

Obtaining a scholarship is the desire of every student or student who studies, especially those who come from poor families. The scholarship can lighten the burden on parents who pay for these students and can streamline the lecture process. However, students do not know exactly what they have to do to get the scholarship. Aside from that, students naturally want to know what causes and conditions have the greatest impact on achievement. The objective of this research is how to predict which number of students among them are predicted to get a scholarship at the opening of the scholarship acceptance using the K-Means and C4.5 methods. Apart from that, the aim of this research is to discover how the K-Means algorithm conducts data clustering (clustering) of student data to determine if they will succeed or not, as well as how the C4.5 algorithm makes predictions against students who have been clustered together. The Rapid Miner program version 9.7.002 was used to process the data in this report. The results of this study were that out of 100 students, 32 students were not scholarship recipients and 68 students were scholarship recipients. Another result of this research is that out of 100 students it is predicted that 9 (9%) will receive scholarships and 91 (91%) will not receive scholarships.
Hybrid Data Mining with the Combination of K-Means Algorithm and C4.5 to Predict Student Achievement Agung Ramadhanu; Sarjon Defit; Shahab Wahhab Kareem
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (480.052 KB) | DOI: 10.29099/ijair.v6i1.225

Abstract

Getting academic achievement is the dream of every student who studies at higher education, especially undergraduate level. Undergraduate students aspire to the highest achievement (champion) at the last achievement of their studies. However, students cannot predict whether these students with the habits that have been done and the current conditions will make them excel or not. Apart from that, of course, students also want to know what factors and conditions influence the achievement the most. The objective to be achieved in this research is how to predict which number of students among them are predicted to excel (champion) at the end of the semester with a combination of the K-Means and C4.5 methods. Besides, the purpose of this study reveals how the K-Means algorithm performs data clustering of student data who will excel or not and how the C4.5 algorithm predicts students who have been grouped. Data processing in this study uses the Rapid Miner software version 9.7.002. The result of this research is that it is easier to group data in numerical form than data in polynomial form. Other results in this study were that out of 100 students, 27 students (27%) were predicted to excel (champions) and 73 (73%) did not achieve (not champions).
Co-Authors Abdul Azis Said Adek Putri Adi Gunawan Adi Gunawan, Adi Agung Ramadhanu Agus Perdana Windarto Ahmad Zamsuri, Ahmad Am, Andri Nofiar Amran Sitohang Andri Nofiar Angga Putra Juledi Anggrawan, Anthony ardialis Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bisma Okmarizal Bosker Sinaga Daeng Saputra Perdana Daniel Theodorus Dayla May Cytry Dendi Ferdinal Deno Yulfa Ardian Dhena Marichy Putri Dinda Permata Sukma Dwi Utari Iswavigra Dwiki Aulia Fakhri Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Elda, Yusma eriwandi Fadlul Hamdi Faisal Roza Fanny Septiani Bufra Fauzan Azim Fauzi Erwis Febri Aldi Febri Hadi Febrina, Yerri Kurnia Fitriani, Yetti Fristi Riandari Fristi Riandari Fuad El Khair Gunadi Nurcahyo Gunadi Widi Nurcahyo Habdi Habdi Halifia Hendri Handika, Yola Tri Haris Kurniawan Hasmaynelis Fitri Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Indah Savitri Hidayat Ira Nia Sanita Ismail Virgo Jefdy Kurniawan Jeri Wandana Juansen, Monsya Juledi, Angga Putra Khairul Azmi Kurniawan, Jefdy L. J. Muhammad Larissa Navia Rani Leoni Lidya M Syahputra M. Ibnu Pati Mardayatmi, Suci Mardison Mardison Mardison Meilinda Sari Meilinda Sari Melissa Triandini Mhd Hary Kurniawan Miftahul Hasanah Miftahul Hasanah, Miftahul Mike Zaimy Monsya Juansen MUHAMMAD TAJUDDIN Nadya Alinda Rahmi Nandel Syofneri Nanik Istianingsih Nopi Purnomo Nori Sahrun, Nori Novi Yanti Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhidayat Pati, Muhammad Ibnu Putra, Rahman Arief Putri, Adek R Rahmiyanti Rafika Sani Rafiska, Rian Rahmad Aditiya Rahman Arief Putra Ramadhan, Mukhlis Ramdani Bayu Putra Rezki - Rezki Rusydi Rian Kurniawan Rianti, Eva Rio Andika Malik Ritna Wahyuni Riyan Ikhbal Salam Rizki Mubarak Rusdianto Roestam S Sumijan Salam, Riyan Ikhbal Sandrawira Anggraini Sandy Mulyanda Setiawan, Adil Shahab Wahhab Kareem Sharon Shaza Alturky Sirait, Weri Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sri Dewi Sri Dewi Suci Mardayatmi Suhefi Oktarian Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Surya Dwi Putra Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syahputra, M Syaljumairi, Raemon Virgo, Ismail Vivi Suryani Wahyuni, Ritna Wanto, Anjar Wenni Afrodita Weri Sirait Y Yuhandri Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yuli Hartati Yunus, Yuhandri Yusma Elda Zulvitri, Z Zurni Mardian