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All Journal Statistika EDU-MAT: Jurnal Pendidikan Matematika Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal Pendidikan Matematika (JUDIKA EDUCATION) Jambura Journal of Mathematics METIK JURNAL Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat JP2M (Jurnal Pendidikan dan Pembelajaran Matematika) Bubungan Tinggi: Jurnal Pengabdian Masyarakat Madaniya Primatika : Jurnal Pendidikan Matematika Journal of Mathematics Education and Science Jurnal Kajian Matematika dan Aplikasinya Ethnomathematics Journal Prosiding Seminar Nasional Pendidikan Matematika, Universitas Mulawarman Jurnal Pendidikan dan Pengabdian Masyarakat JME (Journal of Mathematics Education) Eksponensial Educational Studies: Conference Series Scientific Timeline Jurnal Riset Pecinta Matematika Jurnal Inovasi Refleksi Profesi Guru
Nanda Arista Rizki
Universitas Mulawarman
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Journal : METIK JURNAL

 1 
Fuzzy Inference System Using Tsukamoto Method For Making Decision of Production (Case Study: PT Waru Kaltim Plantation) Dominggus Norvindes Dellas; Ika Purnamasari; Nanda Arista Rizki
METIK JURNAL Vol 4 No 2 (2020): METIK Jurnal
Publisher : LP3M Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v4i2.171

Abstract

The decision-making process using a fuzzy inference system (FIS) logic can use one of the methods called the Tsukamoto method. The process carried out in this method is the same as the fuzzy method in general, namely the formation of fuzzy sets, the fuzzification process, defuzzification, and measuring the accuracy of the result. The purpose of this study was to apply the Tsukamoto method to predict the yield of oil palm production at PT. Waru Kaltim Plantation. Based on the analysis using the Tsukamoto method, 36 fuzzy rules were obtained for each data from February 2013 to December 2015. The prediction results of palm oil production in 2013 did not change, except for May and August. In February, March, June, and August 2014 the level of production is constant, and almost throughout 2015, there was constant. The predicted MAPE for oil palm production was 31,522%, or in the fairly good category.
Perbandingan Klasifikasi Penjurusan Peserta Didik pada Model Diskriminan dan Regresi Logistik Multinomial Nanda Arista Rizki; Petrus Fendiyanto; Ainun Jariah
METIK JURNAL Vol 4 No 2 (2020): METIK Jurnal
Publisher : LP3M Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v4i2.177

Abstract

Pandemi covid-19 yang mengancam hingga seluruh penjuru dunia, membuat kebijakan mengenai Ujian Nasional dihapuskan. Hal ini berakibat terhadap sistem penjurusan di SMAN 2 Samarinda hanya berdasarkan nilai ujian sekolah dan peminatan saja. Hasil penjurusan yang dilakukan oleh sekolah dapat dimodelkan melalui metode klasifikasi. Penelitian ini bertujuan untuk membandingkan ketepatan hasil prediksi penjurusan dari diskriminan dan regresi logistik multinomial. Data yang digunakan dalam penelitian ini adalah nilai ujian sekolah dan hasil penjurusan peserta didik kelas X di SMAN 2 Samarinda. Tahapan analisis yang dilakukan adalah statistika deskriptif, pembentukan model analisis diskriminan, pembentukan model regresi logistik multinomial, penentuan model terbaik, dan interpretasi model terbaik. Proporsi pembagian data training dan data testing yang diterapkan dalam penelitian ini adalah 60:40, 70:30, 80:20, dan 90:10 dengan resampling bootstrap B=1000. Berdasarkan hasil analisis penelitian, maka model regresi logistik multinomial dipilih sebagai model terbaik yang menggambarkan kondisi penjurusan peserta didik di SMAN 2 Samarinda. Hal ini dikarenakan tingkat akurasinya lebih tinggi dibandingkan pada model diskriminan untuk setiap kemungkinan dalam pembagian proporsi data training dan testing. Dengan demikian, semakin tinggi nilai mata pelajaran Bahasa Indonesia, Matematika, dan IPS, maka hasil penjurusannya cenderung tidak pada jurusan Bahasa dan budaya. Namun semakin tinggi nilai Bahasa Inggris, maka hasil penjurusannya cenderung terletak pada jurusan Bahasa dan budaya. Perbedaan antara jurusan IPS dan jurusan MIPA dibanding jurusan Bahasa dan budaya, nampak pada nilai mata pelajaran IPA.
Implementasi Algoritma K-Means Untuk Mengelompokkan Mahasiswa Program Studi Pendidikan Matematika Berdasarkan Sumber Belajarnya Rizki, Nanda Arista; Kurniawan, Kurniawan; Hasan, Isran K.; Sampe, Nofia
METIK JURNAL Vol 7 No 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.584

Abstract

Students must be able to utilize learning resources properly to improve academic achievement. Students can be grouped based on the learning resources they use frequently. Grouping results are helpful for lecturers in designing, evaluating, and analyzing learning in the classroom. This research aimed to implement the K-Means algorithm to classify student learning resources and determine which learning resources determine which groups. The population of this research were students of the Mathematics Education study program at Mulawarman University who are still taking courses. At the same time, the sample were active students from classes 2019, 2020, 2021, and 2022 of the Mathematics Education Study Program at Universitas Mulawarman who were still taking courses and were willing to fill out the questionnaire, namely as many as 111 Students. The data analysis used was clustering analysis using the K-Means algorithm with the Elbow method. New dummy data was formed from learning resource data because it was multiple choice. Based on the results, three main groups were obtained according to the use of learning resources. The learning resources that determine the distribution of groups were electronic books and journals. The first group used electronic books and journals, while the third group did not use either. While the second group only used electronic books. The Silhouette value for this cluster model was 0.615. The classification was classified as good.
Implementasi Algoritma K-Means Untuk Mengelompokkan Mahasiswa Program Studi Pendidikan Matematika Berdasarkan Sumber Belajarnya Rizki, Nanda Arista; Kurniawan, Kurniawan; Hasan, Isran K.; Sampe, Nofia
METIK JURNAL (AKREDITASI SINTA 3) Vol. 7 No. 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.584

Abstract

Students must be able to utilize learning resources properly to improve academic achievement. Students can be grouped based on the learning resources they use frequently. Grouping results are helpful for lecturers in designing, evaluating, and analyzing learning in the classroom. This research aimed to implement the K-Means algorithm to classify student learning resources and determine which learning resources determine which groups. The population of this research were students of the Mathematics Education study program at Mulawarman University who are still taking courses. At the same time, the sample were active students from classes 2019, 2020, 2021, and 2022 of the Mathematics Education Study Program at Universitas Mulawarman who were still taking courses and were willing to fill out the questionnaire, namely as many as 111 Students. The data analysis used was clustering analysis using the K-Means algorithm with the Elbow method. New dummy data was formed from learning resource data because it was multiple choice. Based on the results, three main groups were obtained according to the use of learning resources. The learning resources that determine the distribution of groups were electronic books and journals. The first group used electronic books and journals, while the third group did not use either. While the second group only used electronic books. The Silhouette value for this cluster model was 0.615. The classification was classified as good.
Augmentasi Citra Pohon Kelapa Sawit untuk Deteksi Objek Berbasis Deep Learning Dedy Mirwansyah; Achmad Solichin; Fahrullah; Hardi, Richki; Wulan Sari, Nariza Wanti; Arista Rizki, Nanda; Aldo, Dasril
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1001

Abstract

Penelitian ini menitikberatkan pada Augmentasi citra pohon kelapa sawit untuk deteksi objek menggunakan pendekatan Deep Learning. Pohon kelapa sawit memiliki peran penting dalam industri perkebunan dan pertanian, sehingga pengembangan metode deteksi pohon kelapa sawit yang efisien menjadi krusial dalam pemantauan perkebunan dan pengelolaan sumber daya alam. Metode penelitian melibatkan augmentasi citra, seperti flip, crop, hue, saturation, brightness, exposure dan pra-pemrosesan auto orient dan resize untuk meningkatkan kualitas data pelatihan. Model Deep Learning yang digunakan adalah Convolutional Neural Network (CNN) yang terintegrasi dengan teknik object detection, memungkinkan identifikasi pohon kelapa sawit dari latar belakang dengan akurasi tinggi. Penelitian ini menggunakan 101 citra kepala sawit dan setelah dilakukan augmentasi berjumlah 253 citra pohon kelapa sawit yang bervariasi dalam kondisi pencahayaan, sudut pandang, dan penutupan daun. Hasil eksperimen menunjukkan bahwa metode ini mampu mengidentifikasi pohon kelapa sawit dengan akurasi yang baik, bahkan dalam kondisi yang kompleks. Hasil penelitian ini memiliki potensi aplikasi dalam pemantauan perkebunan kelapa sawit, perencanaan lahan, dan pemantauan lingkungan. Dengan peningkatan akurasi deteksi dan ekstraksi, manajemen perkebunan dan pemantauan lingkungan dapat menjadi lebih efisien dan berkelanjutan.
Co-Authors Abdul Basir Abbas Achmad Solichin Aditama, Cahyanti Ainun Jariah Akbar Maulana Andrea Tri Rian Dani Anwar, Andi Muhammad Aras, Silvia Asyril Asyril Asyril, Asyril Auliaul Fitrah Samsuddin Auliaul Fitrah Samsudin Azainil Azainil Barung, Thesalonica Graina Cahyanti, Siti Nur Carolina Fadia Dewi Dani, Andrea Tri Rian Dasril Aldo Dedy Mirwansyah Devi Fitriyati Dewi, Carolina Fadia Dhira Syahlafandi Dominggus Norvindes Dellas Dwi Rizki Ramadhan Fachry Abda El Rahman Fahrullah Fahrullah Faturachman, Faturachman Haeruddin, Haeruddin Haryaka, Usfandi Ika Purnamasari Ikmawati Ikmawati Ikmawati Ikmawati Isran K Hasan Ivan Novri Krisna Rendi Awalludin Kurniawan Kurniawan Kurniawan Kurniawan Kurniawan Kurniawan Makrina Tindangen Memi Nor Hayati Miftahul Chair Muhammad Hafizh Naufal Yahya Muhammad Nor Abdul Rajak Muhtadin, Achmad Mukhammad Solikhin Mumtaza, Mutiara Mutiara Mumtaza Nainggolan, Sahat Pandapotan Nariza Wanti Wulan Sari Novri, Ivan Nur Rizki Wahidah Nurul Afifah Nurul Afifah Petrus Fendiyanto Prahesti, Selvia Shinta Ricca Noviani Richki Hardi Rusdiana Rusdiana Rusdiana Rusdiana Safitri, Sherina Ayu Sampe, Nofia Siti Najmiah Sri Wahyuningsih Sugiarto S Suyitno Suyitno Syahlafandi, Dhira Syaripuddin Syaripuddin Thesalonica Graina Barung Thesalonica Graina Barung Untu, Zainuddin Untu Wahyuni, Nanda Anggun Watulingas, Jefferson R. Yuki Novia Nasution Yuki Novia Nasution Yuki Novia Nasution Yurin Febria Suci