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All Journal Journal of Computing and Informatics Research Bulletin of Artificial Intelligence International Journal of Informatics and Data Science
Putrama Alkhairi
STIKOM Tunas Bangsa, Pematang Siantar
Computer Science & IT Control & Systems Engineering

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Application of the ANN Algorithm to Predict Access to Drinkable Water in North Sumatra Regency/City Muhammad Alfahrizi Lubis; Deza Geraldin Salsabilah Saragih; Indah Dea Anastasia; Agus Perdana Windarto; Putrama Alkhairi
International Journal of Informatics and Data Science Vol. 1 No. 1 (2023): December 2023
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The increase in population has an impact on increasing the need for drinking water, but this is not in line with the fact that not 100% of the people in Indonesia physically receive or consume safe drinking water. This analysis is based on data from the Central Statistics Agency to look at the social, economic and demographic factors of households regarding the availability of adequate physical quality drinking water. This research aims to predict the percentage of households that have access to adequate drinking water using the Artificial Neural Network (ANN) method. The technique used is Backpropogation. Backrpopagation is a supervised neural network training method, it evaluates the error contribution of each neuron after a set of data has been processed. The goal of backpropagataion is to modify weights to train a neural network to map arbitrary inputs to outputs correctly. Therefore, looking at the above problems, this research aims to determine access to adequate drinking water sources by predicting which households have adequate drinking water so that there is no lack of adequate drinking water sources in the City Regency area. Methods and basic data are needed to make predictions. In this research, data was obtained from BPS which used data from 2014 - 2021, with training data from 2014 - 2020 and testing data from 2015 - 2021. Based on the best architecture produced in this research, namely the 6-17-1 architecture with an accretion of 90%. Thus it can be concluded that the Backpropagation Neural Network can provide good accuracy in carrying out the prediction process.
Sistem Pendukung Keputusan Pemilihan Merek Body Lotion Lokal Terbaik untuk Mencerahkan Kulit dengan Menggunakan Metode MAUT Nurul Aisyah; Selly Andari; Ririn Nadya Utari; Nadya; Dedy Hartama; Putrama Alkhairi
Journal of Computing and Informatics Research Vol 5 No 2 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v5i2.2636

Abstract

This study aims to determine the best local body lotion brand for skin brightening using the Multi-Attribute Utility Theory (MAUT) method. MAUT was chosen for its capability to process data based on various criteria such as benefits, quality, effectiveness, price, and brand reputation. Data were collected through online questionnaires distributed via Google Forms and shared on social media. From 36 alternatives, five local brands were selected for analysis: Marina, Citra, Scarlett, Natur-e, and Herborist. The analysis process involved determining the weight of each criterion, matrix normalization, utility evaluation, and alternative ranking. The results indicate that Marina ranks first with a score of 19, followed by Scarlett (8.7) and Citra (8.1). The MAUT method has proven effective in supporting decisions regarding the selection of the best local body lotion brand, providing objective and structured guidance for consumers.
Analisis Sentimen Pengaruh Media Sosial Terhadap Keputusan Pembelian Konsumen Menggunakan Metode K-Nearest Neighbors (K-NN) Adinda Febiola; Ratih Manalu; Retno Ajeng Kartika Said; Putrama Alkhairi
Bulletin of Artificial Intelligence Vol 5 No 1 (2026): April 2026
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/buai.v5i1.238

Abstract

Perkembangan media sosial telah mengubah cara konsumen berinteraksi dan mengambil keputusan pembelian. Media sosial menjadi platform utama bagi konsumen untuk berbagi pengalaman, memberikan ulasan, dan mendiskusikan produk atau layanan. Penelitian ini bertujuan untuk menganalisis sentimen konsumen terhadap ulasan produk dimedia sosial serta dampaknya pada keputusan pembelian menggunakan metode K-NN. Data yang digunakan berupa ulasan konsumen yang dikumpulkaan pada platform media sosial. Data tersebut diproses melalui tahapan preprocessing, seperti tokenisasi, penghapusan kata tidak penting, dan stemming, sebelum diklasifikasikan menjadi sentimen positif dan negatif. K-NN dipilih karena keandalannya dalam klasifikasi berbasis jarak dan kemampuannya menangani datasetdengan pola distribusi yang kompleks. Hasil penelitian menunjukkan bahwa metode K-NN mampu mengklasifikasikan sentimen konsumen dengan akurasi 100%. Hasil analisis menunjukkan bahwa sentimen positif berkonstribusi signifikan dalam mempengaruhi keputusan keputusan pembelian, sedangkan sentimen negatif cenderung mengurangi minat konsumen. Hasil yang diperoleh dari tahap modelling dengan menggunakan metode K-NN dan perbandingan 80:20 untuk data training dan testing, maka nilai akurasi yang dihasilkan sebesar 90%, precision 90%, recall 100%. Evaluasi model dilakukan menggunakan matrik akurasi, presisi dan recall, dengan hasil yang menunjukkan kinerja K-NN cukup baik dalam mengklasifikasikan sentimen konsumen. Penelitian ini memberikan wawasan tentang pentingnya memahami sentimen konsumen di media sosial untuk strategi pemasaran yang lebih efektif, serta menunjukkan potensi penerapan machine learning, khususnya K-NN dalam menganalisis data tekstual untuk mendukung pengambilan keputusan bisnis.
Pengambilan Keputusan Dalam Pemilihan Penerima Bantuan Sosial Dengan Metode Multi-Factor Evaluation Process Muhammad Azhari; Ahmad Iqbal; Sindy Khairi Purba; Putrama Alkhairi
Bulletin of Artificial Intelligence Vol 4 No 2 (2025): October 2025
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/buai.v4i2.239

Abstract

Social assistance program is one of the government initiatives to reduce poverty level in Indonesia. In Simalungun Regency, Karang Anyar Village, the large amount of recipient data that needs to be processed can slow down the distribution of aid to people in need. This study aims to develop a system that can support social assistance facilitators in determining the right recipients quickly and accurately. The method used in this study is the Multi-Factor Evaluation Process (MFEP), which is expected to be able to identify recipients more efficiently. The data analyzed in this study consists of 25 prospective social assistance recipients obtained from social assistance facilitators in Karang Anyar Village. The assessment criteria for aid recipients include the presence of family members of Early Childhood, Pregnant Women, Elderly, People with Disabilities, High School Children (SMA), Middle School Children (SMP), and Elementary School Children (SD). The MFEP stages include determining the weight for each criterion, assigning a value to each factor, calculating the evaluation weight, and summing all evaluation weights to obtain the final value as a basis for decision making. Data processing on 25 potential aid recipients revealed that 24 were eligible and 6 were ineligible. The results of this data processing using the MFEP method, compared with manual data from social assistance facilitators in Simalungun, Karang Anyar Village, showed a 100% accuracy rate for decision-making. With this level of accuracy, the MFEP method has been proven to assist the decision-making process in selecting appropriate social assistance recipients in Simalungun Regency, Karang Anyar Village.
Co-Authors Adinda Febiola Agus Perdana Windarto Ahmad Iqbal Dedy Hartama Deza Geraldin Salsabilah Saragih Indah Dea Anastasia Muhammad Alfahrizi Lubis Muhammad Azhari Nadya Nurul Aisyah Ratih Manalu Retno Ajeng Kartika Said Ririn Nadya Utari Selly Andari Sindy Khairi Purba