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Pramuko Aji

Telkom University

Author-ID : 563199

Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection Pramuko Aji; Dedy Rahman Wijaya; Elis Hernawati; Sherla Yualinda; Sherli Yualinda; Muhammad Akbar Haikal Frasanta; Rathimala Kannan
IJAIT (International Journal of Applied Information Technology) Vol 7 No 02 (2023): Vol 07 No 02 (November 2023)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v7i02.5374

The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.

Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection Pramuko Aji; Dedy Rahman Wijaya; Elis Hernawati; Sherla Yualinda; Sherli Yualinda; Muhammad Akbar Haikal Frasanta; Rathimala Kannan
IJAIT (International Journal of Applied Information Technology) Vol 07 No 02 (November 2023)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v7i02.5374

The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.

Pelatihan Aplikasi Pencatatan Usaha Tani Berbasis Peta kepada Para Penyuluh Pertanian di BPP Selaawi Kab Garut Fahrudin, Tora; Sukawati, Renny; Aji, Pramuko; Gunawan, Tedi
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 4 (2025): Juni
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i4.2398

Pertanian di Indonesia masih didominasi oleh metode tradisional dengan pencatatan usaha tani yang minim, sehingga menyulitkan petani dalam mengevaluasi keberhasilan usahanya. Menanggapi permasalahan ini, dikembangkan aplikasi U-Tani sebagai solusi digital untuk pencatatan usaha tani secara cerdas dan presisi. Aplikasi ini dilengkapi dengan fitur clustering berbasis teknik data mining untuk mengelompokkan capaian usaha kelompok tani secara dinamis. Pengembangan U-Tani melibatkan kerja sama dengan UPT Pertanian Wilayah V Kabupaten Garut, khususnya Balai Penyuluhan Pertanian (BPP) Kecamatan Selaawi sebagai mitra dalam pengujian prototipe. Kegiatan ini merupakan kelanjutan dari Penelitian Internal Kerjasama Dalam Negeri 2024-1 dan difokuskan pada penambahan modul pemetaan profil usaha tani berbasis GIS. Modul ini diharapkan dapat memberikan manfaat nyata bagi mitra dan masyarakat petani, melalui visualisasi capaian usaha tani dalam bentuk peta serta kemudahan pencatatan dan pelaporan usaha tani secara real time melalui pelatihan. Hasil survei menunjukkan 88.8% materi, waktu pelaksanaan, penjelasan materi, pelayanan panitia, serta harapan untuk keberlanjutan program sudah sesuai dengan kebutuhan.

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