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All Journal Jurnal Ilmiah Informatika Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Statistika dan Komputasi (STATKOM)
Suprayogi, Muhammad Azis
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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KLASIFIKASI PENGADUAN LARAS ONLINE BERBASIS TEXT MINING MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR DAN NAÏVE BAYES Suprayogi, Muhammad Azis
Jurnal Ilmiah Informatika Komputer Vol 26, No 1 (2021)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2021.v26i1.3397

Abstract

Laras Online adalah fasilitas pada Pemkab Bogor yang diperuntukkan bagi masyarakat terutama warga Kabupaten Bogor sebagai wadah aspirasi dan pengaduan. Seiring dengan jumlah pengaduan yang masuk sangat banyak sehingga mengakibatkan waktu yang lebih lama yang digunakan oleh petugas admin dalam memilah kemudian menentukan unit tujuan pengaduan. Penelitian ini bertujuan untuk membandingkan performansi klasifikasi dokumen pengaduan pada situs Laras Online menggunakan algoritma K-Nearest Neighbor (KNN) dan  Naïve Bayes Classifier (NBC). Penelitian dilakukan dengan cara mengumpulkan dokumen pengaduan, melakukan preprocessing, pembobotan kata, klasifikasi, dan pengujian. Pengujian menggunakan cross validation dengan parameter k-fold=10 dan confusion matrix berdasarkan nilai accuracy, precission, recall, dan score-f1. Hasil pengujian terhadap 360 dataset menunjukkan bahwa algoritma NBC lebih baik dari algoritma KNN dengan nilai k=3, k=5, k=7, dan k=9 untuk mengklasifikasikan dokumen pengaduan ke dalam 6 kategori. Hasil klasifikasi menggunakan algoritma NBC memberikan nilai accuracy sebesar 79,16% dengan nilai precission tertinggi pada 2 kategori yaitu Dinsos 91,30% dan SatpolPP 66,80%, nilai recall tertinggi pada 4 kategori yaitu Disdukcapil 89,90%, Dislinghidup 88,40%, Dispupr 93,20%, dan Dishub 76,50%, serta nilai score-f1 tertinggi pada 4 kategori yaitu Disdukcapil sebesar 82,10%, Dislinghidup 82,90%, Dinsos 88,90%, dan Dishub 81,20%.
Model Double Exponential Smoothing Dalam Peramalan Penerimaan Pajak Pemerintah Pusat Indonesia Suprayogi, Muhammad Azis
Jurnal Statistika dan Komputasi Vol. 1 No. 2 (2022): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v1i2.1233

Abstract

Latar   Belakang: Peramalan sebagai salah satu cara memprediksi suatu peristiwa atau nilai tertentu di masa depan dengan cara mempertimbangkan data di masa lalu. Peramalan dibutuhkan untuk memprediksi nilai total penerimaan pajak di masa depan pada tingkat pemerintah pusat dengan tujuan untuk merencanakan keuangan khususnya perencanaan nilai pembiayaan negara untuk kebutuhan belanja pemerintah pusat pada tahun berikutnya. Tujuan:  Meramal nilai penerimaan pajak pada Anggaran Pendapatan dan Belanja Negara (APBN) Pemerintah Pusat di Indonesia menggunakan model double exponential smoothing (DES) dari Holt dan model double exponential smoothing (DES) dari Brown. Metode: Menggunakan metode kuantitatif yaitu melakukan peramalan berdasarkan data deret waktu menggunakan model DES dua parameter dari Holt dan model DES satu parameter dari Brown. Membandingkan kedua model berdasarkan nilai Mean Absolute Percentage Error (MAPE) yang paling kecil. Hasil: Peramalan terbaik model DES dari Brown pada nilai parameter α=0,4 dengan nilai MAPE=4,529%. Peramalan terbaik model DES dari Holt pada nilai parameter α=0,8 dan β=0,6 dengan nilai MAPE=6,966%. Kesimpulan: Model DES dari Brown dan model DES dari Holt dapat digunakan untuk peramalan penerimaan pajak pada APBN Pemerintah Pusat dengan kriteria MAPE sangat baik yaitu < 10%. Adapun model terbaik dari dua metode tersebut adalah model DES satu parameter dari Brown dengan nilai MAPE terkecil sebesar 4,529%.  
A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA Setiawan, Erwan; Suprayogi, Muhammad Azis; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp129-140

Abstract

The Public Health Development Index (Indeks Pembangunan Kesehatan Masyarakat - IPKM) is a combined parameter that reflects progress in health development and is useful for determining areas that need assistance in improving health development. Through IPKM modeling, factors that significantly influence regional public health development can be discovered. This research aims to find an appropriate model for modeling IPKM and determine the factors that significantly influence public health development. The data used is the 2018 IPKM data collected from 119 cities/regencies in Java. We propose three models namely logistic regression (LR), mixed logistic regression (MLR), and geographically weighted logistic regression (GWLR). The research results show that the MLR is the best model for modeling IPKM in Java based on the AIC value criteria. Based on the MLR model, the factors that have a significant influence on public health development are the egg and milk consumption level and the percentage of the number of doctors per thousand population.
GEOGRAPHICALLY WEIGHTED MACHINE LEARNING MODEL FOR ADDRESSING SPATIAL HETEROGENEITY OF PUBLIC HEALTH DEVELOPMENT INDEX IN JAVA ISLAND Suprayogi, Muhammad Azis; Sartono, Bagus; Notodiputro, Khairil Anwar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2577-2588

Abstract

Random Forest (RF) machine learning models have emerged as a prominent algorithm, addressing problems arising from the sole use of decision trees, such as overfitting and instability. However, conventional RF has global coverage that may need to capture spatial variations better. Based on the analysis of the level of public health development, the relationship between the level of health development and risk factors can vary spatially. We use a modified RF algorithm called Geographically Weighted Random Forest (GW-RF) to address this challenge. GW-RF, as a tree-based non-parametric machine learning model, can help explore and visualize relationships between the Public Health Development Index (PHDI) as response variables and factors that are indicators at the district level. GW-RF output is compared with global output, which is RF in 2018 using the percentage of the population with access to clean/decent water (X1), consumption of eggs and milk per capita per week (X2), number of healthcare facilities per 1000 people (X3), number of doctors per 1000 people (X4), pure participation rate ratio female/male (X5), percentage of households that have hand washing facilities with soap and water (X6) as independent variables. Our results show that the non-parametric GW-RF model shows high potential for explaining spatial heterogeneity and predicting PHDI versus a global model when including six major risk factors. However, some of these predictions mean little. Findings of spatial heterogeneity using GW-RF show the need to consider local factors in approaches to increasing PHDI values. Spatial analysis of PHDI provides valuable information for determining geographic targets for areas whose PHDI values need to be improved.
Co-Authors Anang Kurnia Bagus Sartono Erwan Setiawan, Erwan