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INDONESIA
Jurnal Aplikasi Statistika & Komputasi Statistik
Published by Politeknik Statistika STIS
ISSN : 20864132     EISSN : 26151367     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
Redaksi menerima karya ilmiah atau artikel penelitian mengenai kajian teori statistika dan komputasi statistik pada bidang ekonomi dan sosial dan kependudukan, serta teknologi informasi. Redaksi berhak menyunting tulisan tanpa mengubah makna subtansi tulisan. Isi jurnal Aplikasi Statistika dan Komputasi Statistik dapat dikutip dengan menyebutkan sumbernya.
Arjuna Subject : -
Articles 143 Documents
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PERANCANGAN PROTOTIPE WEB DISEMINASI SENSUS PERTANIAN 2023 DENGAN RESPONSIVE WEB DESIGN -, Faturrokhman; Farid Ridho
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 1 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i1.444

Abstract

After carrying out the complete enumeration in the 2023 Agricultural Census, BPS is obliged to present the data that has been obtained through data dissemination, one of which uses a website. However, the previous census web dissemination, ST2013, only got usability testing results of 66.19% and mobile device users had difficulty viewing information on the web. Therefore, we need a ST2023 web dissemination that has a responsive design on all screen sizes. To overcome this problem, researchers conducted a study to design a responsive 2023 Agricultural Census Dissemination Web Prototype using Responsive Web Design techniques. The prototype was then evaluated using usability testing and got a result of 81.64%. Responsiveness and compatibility tests on various browsers have also been carried out and got the results that the prototype is responsive on all screen sizes and performs well in most browsers on different devices.
PERAMALAN KASUS HARIAN MONKEYPOX DUNIA BERDASARKAN METODE SUPPORT VECTOR REGRESSION (SVR) Subiyanto, Marcel Laverda; Amanda, Yulia; Fachrian, Muhammad Nadhil; Afriani; Rohim, Achmad Yazid Busthomi; Chamidah, Nur
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 1 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i1.488

Abstract

Monkeypox saat ini menjadi perhatian masyarakat global. Maka, penting untuk mengetahui perkembangan jumlah kasus monkeypox kedepannya. Pada penelitian ini dilakukan peramalan kasus harian monkeypox menggunakan metode Support Vector Regression (SVR) dengan Fungsi Kernel Radial Basis Function (RBF). Data yang digunakan adalah data sekunder berupa deret waktu harian mulai 29 Mei sampai 20 Oktober 2022. Untuk memperoleh parameter optimal pada model SVR, peneliti menggunakan algoritma grid search untuk memprediksi data testing secara akurat. Nilai RMSE pada data training dan testing sebesar 352,3 dan 809,7.
PENERAPAN MODEL-BASED CLUSTERING PADA PENGELOMPOKAN SAHAM BERDASARKAN RASIO KEUANGAN Hasnida, Irena Sekar Dwi; Kusumawati, Rosita
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 1 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i1.510

Abstract

Untuk meminimalkan kerugian dengan tingkat keuntungan tertentu, investor perlu memilih saham potensial agar keuntungan yang diperoleh optimal. Penelitian ini bertujuan untuk mengetahui penerapan Model-Based Clustering (MBC) dalam mengelompokkan perusahaan berdasarkan kinerja keuangan saham. Indikator keuangan yang digunakan yaitu data rasio likuiditas, profitabilitas, dan solvabilitas tahun 2020 untuk perusahaan yang terdaftar pada indeks LQ45. Dari proses clustering, terbentuk 6 cluster dengan nilai BIC -709,3757 dan model optimal terpilih VEV. Berdasarkan nilai rata-rata setiap rasio, cluster 6 merupakan cluster terbaik karena memiliki mayoritas rasio likuiditas dan profabilitas terbaik serta rasio solvabilitas terrendah. Cluster 6 memiliki kemampuan yang tinggi dibandingkan perusahaan cluster lain untuk membiayai kegiatan operasional perusahaan dan memenuhi kewajiban keuangannya jangka pendek.
PENGEMBANGAN LABORATORIUM STATISTIKA BERBASIS ANDROID Hanike, Yusrianti; Nurjannah, Siti
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 1 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i1.521

Abstract

The importance of practicum as a forum for theoretical explanations has strong support in the knowledge transfer process, so it needs to be embodied in the learning process in a form that is easily accessible to students. The development of a statistics-based laboratory is one of the solutions to make it easier for students to access statistical software which is difficult and has a large capacity. The aim of developing an Android-based statistics laboratory is to implement statistical science and is supported by practicality and effectiveness tests for students and lecturers. The research method is carried out through five stages, namely assessment, design, development, implementation, and evaluation. The research samples were taken from two study programs (prodi) at two tertiary institutions in Ambon, namely the Statistics study program at Pattimura University and the Sharia Economics study program at the State Islamic Institute (IAIN) Ambon. The results showed that the Android-based Statistics Laboratory had a significant percentage of the feasibility, practicality and effectiveness of statistical practicum with a feasibility rating of 71%, practicality of 79% and 75% indicating the effectiveness of using the application.
GENERATING SYNTHETIC TRAINING DATASETS USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK TO IMPROVE IMAGES SEGMENTATION Uzma, Iffati; Rani Nooraeni; Takdir
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 1 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i1.532

Abstract

Limited amount of training datasets in deep learning research could impact the accuracy of the resulting models. This situation can cause overfit, so the model cannot work correctly. A conditional Generative Adversarial Network (CGAN) was introduced to generate synthetic data by considering certain conditions. This study aims to generate additional synthetic training datasets to improve the accuracy of the object segmentation model of images. Firstly, we evaluated CGAN-based dataset generator accuracy against several open datasets. Then, we applied the generator to train two object segmentation models, i.e., FCN and CNN U-Net. Our evaluation shows that CGAN can generate synthetic datasets well. Complex datasets require more training iterations. It also improves the validation loss and validation accuracy of both segmentation models, although other metrics still need further improvement
PERBANDINGAN ORDINAL FOREST DAN REGRESI LOGISTIK ORDINAL Yunus, M.; Khairil Anwar Notodiputro; Bagus Sartono
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 2 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i2.399

Abstract

Terdapat banyak metode yang digunakan untuk mengidentifikasi prediktor penting dan memprediksi nilai dari peubah respons ordinal. Namun saat ini, terdapat metode prediksi untuk peubah respons ordinal yang tidak menggunakan sifat ordinal dari peubah respons tersebut. Penelitian ini menggunakan metode ordinal forest dan sebagai pembanding digunakan juga metode regresi logistik ordinal. Nilai akurasi dan kappa metode ordinal forest pada setiap ulangan (5 ulangan) selalu lebih besar dari pada regresi logistik ordinal. Selanjutnya, nilai akurasi dan kappa setiap kelompok berdasarkan PDRB pada metode ordinal forest selalu lebih besar dari pada regresi logistik ordinal. Sehingga didapatkan metode ordinal forest lebih baik digunakan pada data peringkat status indeks desa membangun Provinsi Maluku Utara tahun 2020.
Perancangan Kembali Antarmuka Web BPS dengan Pendekatan User Centered Design Samu, Chairunnisa Fauzia; Maghfiroh, Lutfi Rahmatuti
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 2 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i2.442

Abstract

Pada tahun 2021, BPS menyelenggarakan Survei Kepuasan Pengguna Website (SKPW). Hasil SKPW menunjukkan bahwa aspek pelayanan website BPS secara keseluruhan berada dalam kategori baik, namun masih ada beberapa aspek yang memiliki nilai kepuasan pengguna yang masih kurang dibandingkan dengan harapan pengguna seperti kelengkapan metadata, kesesuaian produk dengan kebutuhan, kemudahan navigasi, kemudahan mencari produk, kehandalan fungsi pencarian, ketertarikan tampilan website, ketepatan penyusunan tata letak, ketersediaan dua bahasa, dan kemudahan mendapatkan panduan penggunaan website. Oleh karena itu, perlu adanya perancangan user interface website BPS yang baru yang dapat memuaskan pengguna website sesuai dengan harapan pengguna. Metode yang digunakan dalam penelitian ini adalah User Centered Design (UCD) dengan menggunakan User Experience Questionnaire (UEQ) sebagai metode evaluasi. Selain itu, dilakukan juga evaluasi kepuasan pengguna terhadap rancangan antarmuka yang baru untuk dibandingkan dengan SKPW 2021. Agar ukuran kepuasan pengguna yang dihasilkan dapat dibandingkan dengan hasil evaluasi SKPW 2021, digunakan metode analisis kepuasan yang sama dengan yang digunakan di SKPW 2021 yaitu Importance Performance Analysis (IPA). User interface baru yang telah dirancang telah dievaluasi ke pengguna sebanyak dua kali iterasi dengan hasil evaluasi terakhir yaitu tingkat kepuasan seluruh aspek pelayanan yang menjadi cakupan penelitian ini telah mengalami peningkatan dibandingkan dengan hasil SKPW 2021.
Peningkatan Kualitas Statistik Resmi Produktivitas Padi melalui Imputasi Data Non-respons Menggunakan Model Aditif Geospasial Ardiansyah, Muhlis
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 2 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i2.443

Abstract

This study is motivated by the non-response problem in the Crop Cutting Survey conducted by the BPS-Statistics Indonesia as the official statistics provider. BPS has a vision of providing quality statistical data for advanced Indonesia. Handling non-response is essential to supporting this vision because non-response can potentially cause some sample characteristics to be unrepresented. This study proposed a non-response data imputation technique through statistical modeling. The proposed model was an additive model with the addition of geospatial smoothing functions of thin plate regression splines (TP) and Gaussian process (GP). Selection of the best model based on the smallest MSEP of 1000 iterations. Then we compared the average rice productivity between listwise deletion and imputation techniques through three scenarios of non-response data. The results showed that the model with the addition of the GP smoothing function gave the best performance with the smallest MSEP. The other results showed that the imputation method of non-response data is better than ignoring non-response. BPS can consider the imputation method to improve the quality of official statistics on rice productivity.
Modeling Multi-Output Back-Propagation DNN for Forecasting Indonesian Export-Import Maharsi, Rengganis Woro; Saputra, Wisnowan Hendy; Roosyidah, Nila Ayu Nur; Prastyo, Dedy Dwi; Rahayu, Santi Puteri
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v16i1.459

Abstract

Introduction/Main Objectives: International trade through the mechanisms of exports and imports plays a significant role in the Indonesian economy, making the timely availability of export and import value data crucial. Background Problems: Export and import values are influenced by inflation and exchange rate factors. Novelty: This study identifies two categories of variables, namely output (export value and import value) and input (inflation rate and the exchange rate of the Rupiah against the US Dollar). Research Methods: the research approach utilizes a Multi-output Deep Neural Network (DNN) with a Back-propagation algorithm to model the input-output relationship. The method can provide forecasting results for two or more bivariate or multivariate output variables. Finding/Results: The modeling analysis results indicate that the optimal model network structure is DNN (3.4). This model successfully predicts output 1 (export value) and output 2 (import value) with Mean Absolute Percentage Error (MAPE) rates of 13.76% and 13.63%, respectively. Additionally, the forecasting results show predicted export and import values for November to be US$ 16,208.13 billion and US$ 15,105.33 billion, respectively. These findings offer important insights into the direction of Indonesia's international trade movement, which can serve as a basis for future economic decision-making.
Unveiling Spatial Disparities: Exploring High-Risk Diarrhea Among Children Under Five Using Geographically Weighted Quantile Regression Syukrilla, Wara Alfa; Andriyana, Yudhie; Verhasselt, Anneleen
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 2 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i2.536

Abstract

We investigate the impact of the percentage of clean water access, the percentage of handwashing habits, and the toilet category factors on the upper quantile of toddlers’ diarrhea risks in Bandung City, Indonesia, using the Geographically Weighted Quantile Regression model on the 75th percentile. The breusch-Pagan test was used to detect spatial heterogeneity. The results show that the significance, strength, and direction of the relationship between diarrhea and its risk factors depend on the location. At the upper quantile, the Panyileukan district is predicted to have the highest diarrhea risk. In this district, all three predictors significantly affect the toddlers’ diarrhea risk, with the variable of the percentage of houses practicing hand washing habits observed to reduce diarrhea risk the most. In conclusion, clean water access, handwashing habits, and toilet category are the potential risk factors for high-risk childhood diarrhea. This method is powerful as it would allow the decision maker to handle the diarrhea problem aptly based on which predictor has a substantial effect at a specific district of interest. And it can be used to investigate the effect of various intervention strategies and effectively allocate the limited available resources according to the most important locations.

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