The Utility of Decision Tree and Analytics Hierarchy Process in Prioritizing of Social Aid Distribution due to Covid-19 Pandemic in Indonesia

The Indonesian government provided various social assistance programs to local governments during Covid-19. One of the difficulties for the local governments in determining candidates for social aid is ensuring that the number of candidates is in balance with the available quota. Therefore, the local governments must select the most eligible candidates. This study proposes a priority model that can provide recommendations for candidates who meet the criteria for social assistance. The six parameters used in this study were: number of dependents, occupation, income, age, Covid status, and citizen status. The model operates in two stages, namely classification followed by ranking. The classification stage is conducted using a decision tree, while the ranking stage is performed conducted using the Analytical Hierarchy Process (AHP) algorithm. The decision tree separates two classes, namely, eligible and non-eligible. In addition, the classification process is also used to determine the dominant attributes and played a role in the modeling. The proposed model generates a list of the most eligible candidates based on our research. These are sorted by weight from greatest to most eligible using five dominant parameters: number of dependents, income, age, Covid status, and citizen status

The Utility of Decision Tree and Analytics Hierarchy Process in Prioritizing of Social Aid Distribution due to Covid-19 Pandemic in Indonesia

The Indonesian government provided various social assistance programs to local governments during Covid-19. One of the difficulties for the local governments in determining candidates for social aid is ensuring that the number of candidates is in balance with the available quota. Therefore, the local governments must select the most eligible candidates. This study proposes a priority model that can provide recommendations for candidates who meet the criteria for social assistance. The six parameters used in this study were: number of dependents, occupation, income, age, Covid status, and citizen status. The model operates in two stages, namely classification followed by ranking. The classification stage is conducted using a decision tree, while the ranking stage is performed conducted using the Analytical Hierarchy Process (AHP) algorithm. The decision tree separates two classes, namely, eligible and non-eligible. In addition, the classification process is also used to determine the dominant attributes and played a role in the modeling. The proposed model generates a list of the most eligible candidates based on our research. These are sorted by weight from greatest to most eligible using five dominant parameters: number of dependents, income, age, Covid status, and citizen status

Analisis Citra Dental Panoramic Radiograph (DPR) pada Tulang Mandibula untuk Deteksi Osteoporosis Menggunakan Metode GLCM – SVM Multiclass (Gray Level Co- Occurrence Matrix – Support Vector Machine Multiclass)

Salah satu pemeriksaan standar yang ditetapkan untuk deteksi osteoporosis adalah DEXA. Akan tetapi, pemeriksaan tersebut mahal dan hasilnya tidak dapat memberikan informasi tentang mikroarsitektur tulang. Oleh karena itu, pada penelitian ini dilakukan pengenalan pola dari citra DPR yang dianalisis pada tulang ramus mandibula. Tujuannya agar dapat mengklasifikasi tulang normal, osteopenia dan osteoporosis melalui tiga tahapan yaitu pre-processing dari adaptive histogram equalization, ekstraksi fitur dengan GLCM dan klasifikasi dengan SVM Multiclass dari adanya hubungan antara perubahan pola trabekula pada tulang rahang dengan fraktur tulang panggul. Jumlah data citra DPR yang digunakan sebanyak 61 data (24 data tulang normal, 24 data tulang osteopenia, dan 13 data tulang osteoporosis) yang terbagi menjadi dua bagian, yakni 75% sebagai data latih dan 25% sebagai data uji. Berdasarkan analisis citra DPR pada ROI ramus mandibula yang digunakan sebagai dasar computer-aided diagnosis sistem telah dapat digunakan sebagai deteksi osteoporosis. Ekstraksi fitur GLCM berdasarkan empat fitur statistik telah menujukkan sudut orientasi terbaik adalah 〖135〗^0 dan jarak d=1 piksel serta SVM Multiclass terbaik dibangun oleh kernel polynomial berderajat dua. Hasil akurasi data uji yang dihasilkan sebesar 81,25%, sensitivitas sebesar 75%, spesifisitas sebesar 90%, dan precision sebesar 88,89%