Judul: Identifikasi Faktor yang Memengaruhi Anak Putus Sekolah di Provinsi Daerah Istimewa Yogyakarta Menggunakan Regresi Logistik Biner
Abstrak: Anak putus sekolah merupakan anak yang tidak mampu menyelesaikan pendidikannya dan berhenti sekolah sebelum masa belajarnya berakhir sehingga belum memiliki tanda tamat belajar atau ijazah pada jenjang pendidikan tersebut. Provinsi DIY merupakan salah satu provinsi yang memiliki kasus putus sekolah dan mengalami peningkatan dari tahun 2018 sampai 2020. Untuk itu, penelitian ini bertujuan memodelkan kejadian anak putus sekolah di Provinsi DIY menggunakan regresi logistik biner dengan menerapkan metode SMOTE untuk menangani ketidakseimbangan data sehingga diketahui faktor-faktor yang memengaruhinya. Regresi logistik biner digunakan karena peubah respon bersifat biner (Y = 1 jika putus sekolah dan Y = 0 jika masih aktif bersekolah). Data penelitian menggunakan data sekunder hasil Survei Sosisal Ekonomi Nasional 2021 yang dilakukan oleh Badan Pusat Statistik wilayah Provinsi DIY. Hasil penelitian menunjukkan faktor yang berpengaruh, yaitu pendidikan kepala rumah tangga, rata-rata pengeluaran rumah tangga perkapita, status bekerja anak, dan status penerima PIP dengan ketepatan klasifikasi model berikut : akurasi 79,16%, F1-score 17,19%, dan AUC 83,22%., Dropout schools are children who are unable to complete their education and leave school before the end of their study period so that they do not obtain certificate from school or a diploma at that level of education. DIY Province is one of the provinces that has dropout cases increasing from 2018 to 2020. For this reason, this study aims to model the incidence of school dropouts in DIY Province using binary logistic regression by applying the SMOTE method to handle data imbalances so that the factors that influence it are known. Binary logistic regression was used because the response variables were binary (Y = 1 if they dropped out of school and Y = 0 if they were still in school). This research used secondary data from the 2021 National Socio-Economic Survey conducted by the Central Statistics Agency for the DIY Province. The results showed that the influencing factors were the education of the head of the household, the average household expenditure per capita, the child’s working status, and the status of PIP recipients with 79,16% accuracy, 17,19% F1-score, and 83,22% AUC.
Keyword: binary logistic regression, school dropout, smote

Judul: Klasifikasi Status Keaktifan Siswa SMA di Jawa Barat Menggunakan Random Forest dengan SMOTE
Abstrak: Tingkat putus sekolah di Indonesia memiliki persentase yang semakin tinggi ketika jenjang pendidikan semakin tinggi. Angka putus sekolah jenjang SMA di Indonesia sebesar 0.67%. Jawa Barat merupakan provinsi dengan angka putus sekolah jenjang SMA tertinggi pada tahun ajaran 2017/2018. Tahun ajaran selanjutnya, angka putus sekolah jenjang SMA di jawa Barat menurun. Siswa yang mengalami putus sekolah disebabkan oleh berbagai macam faktor. Penelitian ini mengkaji peubah penting dan kinerja klasifikasi yang dihasilkan oleh random forest. Jumlah siswa putus sekolah yang sangat sedikit dibandingkan jumlah siswa aktif sekolah menyebabkan ketidakseimbangan pada data. Ketidakseimbangan pada data ditangani menggunakan teknik SMOTE. Random forest dengan SMOTE dinilai mampu memprediksi kelas data dengan lebih baik karena mampu meningkatkan nilai sensitivitas dan menurunkan kesalahan dalam mengklasifikasikan siswa putus sekolah sebagai siswa aktif sekolah. Penghasilan ayah, jumlah saudara, kelas, jenjang pendidikan ayah, dan jenis pekerjaan ayah merupakan peubah-peubah penting yang memberikan pengaruh besar dalam menentukan status keaktifan siswa SMA di Jawa Barat., The dropout rate in Indonesia has a higher percentage as education levels grow. The high school dropout rate in Indonesia is at 0.67%. West Java is the province with the highest high school dropout rate in the academic year 2017/2018. In the next academic year, the high school dropout rate in West Java decreased. The student who drop out of school was caused by various factors. This study examines important variables and classification performance that are generated by random forest. The number of dropout students is very small compared to the number of active students. The imbalance data is handled using SMOTE. Random forest with SMOTE is considered able to predict data classes better because it can increase sensitivity values and reduce errors in classifying dropout students as active students. Father's income, number of siblings, class, father's education level, and father's type of work are important variables that have a major influence in determining the active status of high school students in West Java.
Keyword: Classification, Important variable, Drop out of school, Random forest, SMOTE

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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